{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 14.3 US Baby Names 1880–2010（1880年至2010年美国婴儿姓名）\n",
    "\n",
    "这个数据是从1880年到2010年婴儿名字频率数据。我们先看一下这个数据长什么样子：\n",
    "\n",
    "![](http://oydgk2hgw.bkt.clouddn.com/pydata-book/r5ofm.png)\n",
    "\n",
    "个数据集可以用来做很多事，例如：\n",
    "\n",
    "- 计算指定名字的年度比例\n",
    "- 计算某个名字的相对排名\n",
    "- 计算各年度最流行的名字，以及增长或减少最快的名字\n",
    "- 分析名字趋势：元音、辅音、长度、总体多样性、拼写变化、首尾字母等\n",
    "- 分析外源性趋势：圣经中的名字、名人、人口结构变化等\n",
    "\n",
    "之后的教程会涉及到其中一些。另外可以去官网直接下载姓名数据，[Popular Baby Names](https://www.ssa.gov/oact/babynames/limits.html)。\n",
    "\n",
    "下载National data之后，会得到names.zip文件，解压后，可以看到一系列类似于yob1880.txt这样名字的文件，说明这些文件是按年份记录的。这里使用Unix head命令查看一下文件的前10行："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mary,F,7065\r",
      "\r\n",
      "Anna,F,2604\r",
      "\r\n",
      "Emma,F,2003\r",
      "\r\n",
      "Elizabeth,F,1939\r",
      "\r\n",
      "Minnie,F,1746\r",
      "\r\n",
      "Margaret,F,1578\r",
      "\r\n",
      "Ida,F,1472\r",
      "\r\n",
      "Alice,F,1414\r",
      "\r\n",
      "Bertha,F,1320\r",
      "\r\n",
      "Sarah,F,1288\r",
      "\r\n"
     ]
    }
   ],
   "source": [
    "!head -n 10 ../datasets/babynames/yob1880.txt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "由于这是一个非常标准的以逗号隔开的格式（即CSV文件），所以可以用pandas.read_csv将其加载到DataFrame中："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Make display smaller\n",
    "pd.options.display.max_rows = 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "names1880 = pd.read_csv('../datasets/babynames/yob1880.txt', names=['names', 'sex', 'births'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>names</th>\n",
       "      <th>sex</th>\n",
       "      <th>births</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Mary</td>\n",
       "      <td>F</td>\n",
       "      <td>7065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Anna</td>\n",
       "      <td>F</td>\n",
       "      <td>2604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Emma</td>\n",
       "      <td>F</td>\n",
       "      <td>2003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Elizabeth</td>\n",
       "      <td>F</td>\n",
       "      <td>1939</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Minnie</td>\n",
       "      <td>F</td>\n",
       "      <td>1746</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995</th>\n",
       "      <td>Woodie</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1996</th>\n",
       "      <td>Worthy</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997</th>\n",
       "      <td>Wright</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1998</th>\n",
       "      <td>York</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999</th>\n",
       "      <td>Zachariah</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2000 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          names sex  births\n",
       "0          Mary   F    7065\n",
       "1          Anna   F    2604\n",
       "2          Emma   F    2003\n",
       "3     Elizabeth   F    1939\n",
       "4        Minnie   F    1746\n",
       "...         ...  ..     ...\n",
       "1995     Woodie   M       5\n",
       "1996     Worthy   M       5\n",
       "1997     Wright   M       5\n",
       "1998       York   M       5\n",
       "1999  Zachariah   M       5\n",
       "\n",
       "[2000 rows x 3 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "names1880"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "这些文件中仅含有当年出现超过5次以上的名字。为了简单化，我们可以用births列的sex分组小计，表示该年度的births总计："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sex\n",
       "F     90993\n",
       "M    110493\n",
       "Name: births, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "names1880.groupby('sex').births.sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "由于该数据集按年度被分割成了多个文件，所以第一件事情就是要将所有数据都组装到一个DataFrame里面，并加上一个year字段。使用pandas.concat可以做到："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 2010是最后一个有效统计年度\n",
    "years = range(1880, 2011)\n",
    "\n",
    "pieces = []\n",
    "columns = ['name', 'sex', 'births']\n",
    "\n",
    "for year in years:\n",
    "    path = '../datasets/babynames/yob%d.txt' % year\n",
    "    frame = pd.read_csv(path, names=columns)\n",
    "    \n",
    "    frame['year'] = year\n",
    "    pieces.append(frame)\n",
    "    \n",
    "# 将所有数据整合到单个DataFrame中\n",
    "names = pd.concat(pieces, ignore_index=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "这里要注意几件事。\n",
    "\n",
    "- 第一，concat默认是按行将多个DataFrame组合到一起的；\n",
    "- 第二，必须指定ignore_index=True，因为我们不希望保留read_csv所返回的原始索引。\n",
    "\n",
    "现在我们得到了一个非常大的DataFrame，它含有全部的名字数据。现在names这个DataFrame看上去是："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>sex</th>\n",
       "      <th>births</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Mary</td>\n",
       "      <td>F</td>\n",
       "      <td>7065</td>\n",
       "      <td>1880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Anna</td>\n",
       "      <td>F</td>\n",
       "      <td>2604</td>\n",
       "      <td>1880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Emma</td>\n",
       "      <td>F</td>\n",
       "      <td>2003</td>\n",
       "      <td>1880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Elizabeth</td>\n",
       "      <td>F</td>\n",
       "      <td>1939</td>\n",
       "      <td>1880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Minnie</td>\n",
       "      <td>F</td>\n",
       "      <td>1746</td>\n",
       "      <td>1880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1690779</th>\n",
       "      <td>Zymaire</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1690780</th>\n",
       "      <td>Zyonne</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1690781</th>\n",
       "      <td>Zyquarius</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1690782</th>\n",
       "      <td>Zyran</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1690783</th>\n",
       "      <td>Zzyzx</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1690784 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              name sex  births  year\n",
       "0             Mary   F    7065  1880\n",
       "1             Anna   F    2604  1880\n",
       "2             Emma   F    2003  1880\n",
       "3        Elizabeth   F    1939  1880\n",
       "4           Minnie   F    1746  1880\n",
       "...            ...  ..     ...   ...\n",
       "1690779    Zymaire   M       5  2010\n",
       "1690780     Zyonne   M       5  2010\n",
       "1690781  Zyquarius   M       5  2010\n",
       "1690782      Zyran   M       5  2010\n",
       "1690783      Zzyzx   M       5  2010\n",
       "\n",
       "[1690784 rows x 4 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "names"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "有了这些数据后，我们就可以利用groupby或pivot_table在year和sex界别上对其进行聚合了："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "total_births = names.pivot_table('births', index='year',\n",
    "                                columns='sex', aggfunc=sum)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>sex</th>\n",
       "      <th>F</th>\n",
       "      <th>M</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2006</th>\n",
       "      <td>1896468</td>\n",
       "      <td>2050234</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007</th>\n",
       "      <td>1916888</td>\n",
       "      <td>2069242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008</th>\n",
       "      <td>1883645</td>\n",
       "      <td>2032310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009</th>\n",
       "      <td>1827643</td>\n",
       "      <td>1973359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010</th>\n",
       "      <td>1759010</td>\n",
       "      <td>1898382</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "sex         F        M\n",
       "year                  \n",
       "2006  1896468  2050234\n",
       "2007  1916888  2069242\n",
       "2008  1883645  2032310\n",
       "2009  1827643  1973359\n",
       "2010  1759010  1898382"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_births.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1289ad710>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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pzSPaa0g/RSnE+RKLk9DQ+rQX8FzXR82nydTMzoK9vHz8bX405uEebwlzrdA0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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1289b7b70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "total_births.plot(title='Total births by sex and year', figsize=(15, 8))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "下面我们来插入一个prop列，用于存放指定名字的婴儿数相对于总出生数的比列。prop值为0.02表示每100名婴儿中有2名取了当前这个名字。因此，我们先按year和sex分组，然后再将新列加到各个分组上："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def add_prop(group): \n",
    "    group['prop'] = group.births / group.births.sum()\n",
    "    return group\n",
    "names = names.groupby(['year', 'sex']).apply(add_prop)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "  </thead>\n",
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       "      <th>0</th>\n",
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       "      <th>1</th>\n",
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       "      <td>1880</td>\n",
       "      <td>0.028618</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Emma</td>\n",
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       "      <td>1880</td>\n",
       "      <td>0.022013</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Elizabeth</td>\n",
       "      <td>F</td>\n",
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       "      <td>1880</td>\n",
       "      <td>0.021309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Minnie</td>\n",
       "      <td>F</td>\n",
       "      <td>1746</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.019188</td>\n",
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       "    <tr>\n",
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       "      <th>1690779</th>\n",
       "      <td>Zymaire</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
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       "      <td>0.000003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1690780</th>\n",
       "      <td>Zyonne</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1690781</th>\n",
       "      <td>Zyquarius</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1690782</th>\n",
       "      <td>Zyran</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1690783</th>\n",
       "      <td>Zzyzx</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000003</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1690784 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              name sex  births  year      prop\n",
       "0             Mary   F    7065  1880  0.077643\n",
       "1             Anna   F    2604  1880  0.028618\n",
       "2             Emma   F    2003  1880  0.022013\n",
       "3        Elizabeth   F    1939  1880  0.021309\n",
       "4           Minnie   F    1746  1880  0.019188\n",
       "...            ...  ..     ...   ...       ...\n",
       "1690779    Zymaire   M       5  2010  0.000003\n",
       "1690780     Zyonne   M       5  2010  0.000003\n",
       "1690781  Zyquarius   M       5  2010  0.000003\n",
       "1690782      Zyran   M       5  2010  0.000003\n",
       "1690783      Zzyzx   M       5  2010  0.000003\n",
       "\n",
       "[1690784 rows x 5 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "names"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "在执行这样的分组处理时，一般都应该做一些有效性检查（sanity check），比如验证所有分组的prop的综合是否为1。由于这是一个浮点型数据，所以我们应该用np.allclose来检查这个分组总计值是否够近似于（可能不会精确等于）1："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "year  sex\n",
       "1880  F      1.0\n",
       "      M      1.0\n",
       "1881  F      1.0\n",
       "      M      1.0\n",
       "1882  F      1.0\n",
       "            ... \n",
       "2008  M      1.0\n",
       "2009  F      1.0\n",
       "      M      1.0\n",
       "2010  F      1.0\n",
       "      M      1.0\n",
       "Name: prop, Length: 262, dtype: float64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "names.groupby(['year', 'sex']).prop.sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "这样就算完活了。为了便于实现进一步的分析，我们需要取出该数据的一个子集：每对sex/year组合的前1000个名字。这又是一个分组操作："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def get_top1000(group):\n",
    "    return group.sort_values(by='births', ascending=False)[:1000]\n",
    "\n",
    "grouped = names.groupby(['year', 'sex'])\n",
    "top1000 = grouped.apply(get_top1000)\n",
    "\n",
    "# Drop the group index, not needed\n",
    "top1000.reset_index(inplace=True, drop=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "如果喜欢DIY的话，也可以这样："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "pieces =[]\n",
    "for year, group in names.groupby(['year', 'sex']):\n",
    "    pieces.append(group.sort_values(by='births', ascending=False)[:1000])\n",
    "    \n",
    "top1000 = pd.concat(pieces, ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        text-align: right;\n",
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       "      <th>2</th>\n",
       "      <td>Emma</td>\n",
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       "      <td>Minnie</td>\n",
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       "    <tr>\n",
       "      <th>261872</th>\n",
       "      <td>Camilo</td>\n",
       "      <td>M</td>\n",
       "      <td>194</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000102</td>\n",
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       "    <tr>\n",
       "      <th>261873</th>\n",
       "      <td>Destin</td>\n",
       "      <td>M</td>\n",
       "      <td>194</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>261874</th>\n",
       "      <td>Jaquan</td>\n",
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       "      <td>194</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>261875</th>\n",
       "      <td>Jaydan</td>\n",
       "      <td>M</td>\n",
       "      <td>194</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>261876</th>\n",
       "      <td>Maxton</td>\n",
       "      <td>M</td>\n",
       "      <td>193</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000102</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>261877 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             name sex  births  year      prop\n",
       "0            Mary   F    7065  1880  0.077643\n",
       "1            Anna   F    2604  1880  0.028618\n",
       "2            Emma   F    2003  1880  0.022013\n",
       "3       Elizabeth   F    1939  1880  0.021309\n",
       "4          Minnie   F    1746  1880  0.019188\n",
       "...           ...  ..     ...   ...       ...\n",
       "261872     Camilo   M     194  2010  0.000102\n",
       "261873     Destin   M     194  2010  0.000102\n",
       "261874     Jaquan   M     194  2010  0.000102\n",
       "261875     Jaydan   M     194  2010  0.000102\n",
       "261876     Maxton   M     193  2010  0.000102\n",
       "\n",
       "[261877 rows x 5 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top1000"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "接下来针对这个top1000数据集，我们就可以开始数据分析工作了\n",
    "\n",
    "# 1 Analyzing Naming Trends（分析命名趋势）\n",
    "\n",
    "有了完整的数据集和刚才生成的top1000数据集，我们就可以开始分析各种命名趋势了。首先将前1000个名字分为男女两个部分：\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "boys = top1000[top1000.sex=='M']\n",
    "girls = top1000[top1000.sex=='F']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "这是两个简单的时间序列，只需要稍作整理即可绘制出相应的图标，比如每年叫做John和Mary的婴儿数。我们先生成一张按year和name统计的总出生数透视表："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006</th>\n",
       "      <td>NaN</td>\n",
       "      <td>3737.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8279.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>297.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>404.0</td>\n",
       "      <td>440.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5145.0</td>\n",
       "      <td>2839.0</td>\n",
       "      <td>530.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007</th>\n",
       "      <td>NaN</td>\n",
       "      <td>3941.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8914.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>313.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>349.0</td>\n",
       "      <td>468.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4925.0</td>\n",
       "      <td>3028.0</td>\n",
       "      <td>526.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008</th>\n",
       "      <td>955.0</td>\n",
       "      <td>4028.0</td>\n",
       "      <td>219.0</td>\n",
       "      <td>8511.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>317.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>344.0</td>\n",
       "      <td>400.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4764.0</td>\n",
       "      <td>3438.0</td>\n",
       "      <td>492.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009</th>\n",
       "      <td>1265.0</td>\n",
       "      <td>4352.0</td>\n",
       "      <td>270.0</td>\n",
       "      <td>7936.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>296.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>307.0</td>\n",
       "      <td>369.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5120.0</td>\n",
       "      <td>3981.0</td>\n",
       "      <td>496.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010</th>\n",
       "      <td>448.0</td>\n",
       "      <td>4628.0</td>\n",
       "      <td>438.0</td>\n",
       "      <td>7374.0</td>\n",
       "      <td>226.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>277.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>295.0</td>\n",
       "      <td>324.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6200.0</td>\n",
       "      <td>5164.0</td>\n",
       "      <td>504.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>258.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>131 rows × 6868 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "name   Aaden  Aaliyah  Aarav   Aaron  Aarush  Ab  Abagail  Abb  Abbey  Abbie  \\\n",
       "year                                                                           \n",
       "1880     NaN      NaN    NaN   102.0     NaN NaN      NaN  NaN    NaN   71.0   \n",
       "1881     NaN      NaN    NaN    94.0     NaN NaN      NaN  NaN    NaN   81.0   \n",
       "1882     NaN      NaN    NaN    85.0     NaN NaN      NaN  NaN    NaN   80.0   \n",
       "1883     NaN      NaN    NaN   105.0     NaN NaN      NaN  NaN    NaN   79.0   \n",
       "1884     NaN      NaN    NaN    97.0     NaN NaN      NaN  NaN    NaN   98.0   \n",
       "...      ...      ...    ...     ...     ...  ..      ...  ...    ...    ...   \n",
       "2006     NaN   3737.0    NaN  8279.0     NaN NaN    297.0  NaN  404.0  440.0   \n",
       "2007     NaN   3941.0    NaN  8914.0     NaN NaN    313.0  NaN  349.0  468.0   \n",
       "2008   955.0   4028.0  219.0  8511.0     NaN NaN    317.0  NaN  344.0  400.0   \n",
       "2009  1265.0   4352.0  270.0  7936.0     NaN NaN    296.0  NaN  307.0  369.0   \n",
       "2010   448.0   4628.0  438.0  7374.0   226.0 NaN    277.0  NaN  295.0  324.0   \n",
       "\n",
       "name  ...     Zoa     Zoe    Zoey   Zoie  Zola  Zollie  Zona  Zora  Zula  \\\n",
       "year  ...                                                                  \n",
       "1880  ...     8.0    23.0     NaN    NaN   7.0     NaN   8.0  28.0  27.0   \n",
       "1881  ...     NaN    22.0     NaN    NaN  10.0     NaN   9.0  21.0  27.0   \n",
       "1882  ...     8.0    25.0     NaN    NaN   9.0     NaN  17.0  32.0  21.0   \n",
       "1883  ...     NaN    23.0     NaN    NaN  10.0     NaN  11.0  35.0  25.0   \n",
       "1884  ...    13.0    31.0     NaN    NaN  14.0     6.0   8.0  58.0  27.0   \n",
       "...   ...     ...     ...     ...    ...   ...     ...   ...   ...   ...   \n",
       "2006  ...     NaN  5145.0  2839.0  530.0   NaN     NaN   NaN   NaN   NaN   \n",
       "2007  ...     NaN  4925.0  3028.0  526.0   NaN     NaN   NaN   NaN   NaN   \n",
       "2008  ...     NaN  4764.0  3438.0  492.0   NaN     NaN   NaN   NaN   NaN   \n",
       "2009  ...     NaN  5120.0  3981.0  496.0   NaN     NaN   NaN   NaN   NaN   \n",
       "2010  ...     NaN  6200.0  5164.0  504.0   NaN     NaN   NaN   NaN   NaN   \n",
       "\n",
       "name   Zuri  \n",
       "year         \n",
       "1880    NaN  \n",
       "1881    NaN  \n",
       "1882    NaN  \n",
       "1883    NaN  \n",
       "1884    NaN  \n",
       "...     ...  \n",
       "2006    NaN  \n",
       "2007    NaN  \n",
       "2008    NaN  \n",
       "2009    NaN  \n",
       "2010  258.0  \n",
       "\n",
       "[131 rows x 6868 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_births = top1000.pivot_table('births', index='year', \n",
    "                                   columns='name', aggfunc=sum)\n",
    "\n",
    "total_births"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "接下来使用DataFrame中的plot方法："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 131 entries, 1880 to 2010\n",
      "Columns: 6868 entries, Aaden to Zuri\n",
      "dtypes: float64(6868)\n",
      "memory usage: 6.9 MB\n"
     ]
    }
   ],
   "source": [
    "total_births.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "subset = total_births[['John', 'Harry', 'Mary', 'Marilyn']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([<matplotlib.axes._subplots.AxesSubplot object at 0x1132a4828>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x116933080>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x117d24710>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x117d70b70>], dtype=object)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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L+Meb55w1s5S1zT2s2dlELA5ZaW4y0z1kpXsIR2L879Kd9AUjfPDqqVx+TvlQ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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x117457cc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "subset.plot(subplots=True, figsize=(12, 10), grid=False,\n",
    "            title=\"Number of births per year\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 评价命名多样性的增长\n",
    "\n",
    "上图反应的降低情况可能意味着父母愿意给小孩起常见的名字越来越少。这个假设可以从数据中得到验证。一个办法是计算最流行的1000个名字所占的比例，我们按year和sex进行聚合并绘图："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "table = top1000.pivot_table('prop', index='year',\n",
    "                           columns='sex', aggfunc=sum)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11b325128>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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FRUREREREbl7Gelwksh4XuVqTk4Xr8bI3jM7OzKa+B6r/5q4/6B5s9vqD7oHqv7nrD7oH\nqv/mrj9s/Hvwne98m5/7uZ/il37pP/Oa17yuuf0d7/hB9uzZyy/+4i9d8vzOzsy6lEOjWYqIiIiI\niFylrVu38cUv/kPz+YkTxymVSte0DFedmXMc54eBtOu6H34JyiMiIiIiInJF/uL4Z3hq4rl1vead\nXbfy1l1vvOxxu3btZnj4NAsLC6TTaT73uc/y2te+gfHxsXUtz6VcUTDnuu4Q8EC4/idr7H/VupZK\nRERERETkBvfKVz7EV77yCP/oH72Jw4ef50d+5B03XjAnIiIiIiJyo3nrrjdeURbtpfLww6/nN3/z\n1+nr6+f22++85q+vPnMiIiIiIiIvQH//AKVSiT//8//Da1/7hmv++grmREREREREXqDv+Z6HmZgY\nZ3Bw6zV/bTWzFBERERERuQp33XUPd911DwBvf/sP8va3/yAADzzwIA888OA1K4cycyIiIiIiIhuQ\ngjkREREREZENSMGciIiIiIjIBqRgTkREREREZANSMCciIiIiIrIBKZgTERERERHZgBTMiYiIiIiI\nbEAK5kRERERERDYgBXMiIiIiIiIbkII5ERERERGRDUjBnIiIiIiIyAakYE5ERERERGQDUjAnIiIi\nIiKyASmYExERERER2YAUzImIiIiIiGxACuZEREREREQ2IAVzIiIiIiIiG5CCORERERERkQ1IwZyI\niIiIiMgGpGBORERERERkA1IwJyIiIiIisgEpmBMREREREdmAFMyJiIiIiIhsQArmRERERERENqAr\nCuYcx7nfcZwvr7H9TY7jPOE4zjccx/mJdS+diIiIiIiIrOmywZzjOO8Hfh+Ir9puA/8deC3wSuA9\njuN0vxSFFBERERERkZUiV3DMCeCtwB+t2r4POO667iyA4zhfB74b+LNLXezPvniUhYUKAA2/TtGf\nZ9Gfo+Qv0BptY0tqkNZkilTcJhmPNJcRSy1CRUREREREllw2mHNd91OO42xbY1cWmF/2vADkLne9\nTzz/KYz4YvCIljGMZTvr8Ngi+MUsXqGVRr4Nb6EV6jaJTIVYtoSdLkK8QMPOUzOLdMQ6OdCzm7u2\n7GFPx06ysfTlinBFao0a8+UCc+U8Db/B9pYtRCPRdbk2QGdnZt2utRGp/pu7/qB7sNnrD7oHqv/m\nrj/oHqj+m7v+oHuwHq4kM3cxeWD5TyADzF32BbuHAUhZaXL2IC12Kzm7jZSVYaI0wWh5mJnUGF4q\nT6TndHCSb4LhUQEq4XV8z8Qvxzjnn2Hs9Bm+cPqRoBBmKztbttKZbqHSqFCqVyg3ypTrZSqNCtVG\nDcu0sAyLiBkJHuF6uV4mXy2QrxYo1ksrym0ZFluzA+xq2cHO3DZ2tmwjEUkA0PAaTJdnmChOMVma\nZqI4RdWr0hZvpSPeRlu8lfZEK7loFsu06OzMMDlZoOE1VpQxZSdpiV02Ht7wluq/WW32+oPuwWav\nP+geqP6bu/6ge6D6b+76g+7BegWyLyaYOwzsdhynDVggaGL53y530m++/j9AMUo8ErvoMbVGjaH8\nMMfmTnJs7hSVeoWeVBc9qS56U910xjuJGxnyCzUOnZng2dETDC+eoR6bJp+e4+mZp2HmwusafgTT\nj2CYHj4eHg18/BXHpO0ULbEcg5kBMtEM2Vgaz/c4OXeaofwZTs4HAaaBQW+qm6pXY6Y8i+d7l71h\npmGSi2bxDY9itUTVq11wTG+qm31te9jf5rCzZTtRy16x3/d98tUFxhbHGStOYBkm3clOupKdZKMZ\njBWpThERERERuVlddTDnOM4PA2nXdT/sOM6/BD5HMJDK/3Zdd+Ry52/J9TFZvXQUbls2u1t3srt1\n5yWPa0nFGezO8Hp24vk+56YWOXx6hmdHhpgtFqlVDaplk0rZpFz28f21+t35tGQjdLXFaUulSCei\npA2bpBUhhU3KsGnJRHnLjhR1v8qp+WGOz5/i+NxJhvJniFsxtmW30JnooDPRQVeync5kBzErxkx5\nlpnSLNPlWabLM8yUZ5ktz5OIxMnaGeJWnHgkTjwSI27FmCrPcGz2JI+c+RqPnPkathlhV8sOtmcH\nmavkGSuOc25xgtKqrOGSuBWjK9lBV7KTtngrphHW118ZsNqWTTwSJ2HFSUSCRzySwDJMFmtFFutF\nFmuLLNaKFGslivUSKTtJW7w1yDLGW2iLtxK1gmanhTC4PLc40SxjvpLn/t67efWWV2Cbl36b+b7P\nU5PP8czkQbqTnWzLDrItu4WknbzkeSIiIiIim5nhr/qgfw341yOl6vs+1brHYqnG2EyRc9NFxmaK\njE0vcm6myEy+csnzo7bJjt4sO/tz7B7IsaMvRyoeeUGZsEullWuNGifmhzg043J4+iiji2PNfaZh\n0pnooDfVRU+qm55kF3W/wURxMnxMMVGaou7Vr7pML0TaTgGwUFu8YF/EjFD36nQm2nn77jdzoGNf\nc9/y+p+cP81fHPsMp/KnL7jG+cBukEw0jec3aPge3rJHxIzQn+6jL9WNZVovUU3X12ZvVgC6B5u9\n/qB7oPpv7vqD7oHqv7nrD7oHnZ2ZdWlO92KaWW4ohmEQsy1itkVbNs7+bW0r9leqDQrFKovlOovl\nWrAs1Vgs15icK3NidB53eI4jw+e7Bfa2J8kkbAzDwDQNTIPmum2ZZFJRMgmbbCpKJmmTSQbPa4ZB\nfr6MZRlELBPLNIiE67Zls7dtN3vbdsMumKvMc7YwSlu8la5kB5HLZLk832OmPMdcZf6ix1QbVUr1\noB9hqVGmVA8enu+RiiRI2UmSdpKUnSRlp0hE4ixUF5gpzwXZxvJsc93HZ3tuK71hcNmb6qYr2Ynn\nN/jbU5/nqyPf4EPP/gG3tO/lbbvfRHeyE4Cp0gx/feKzfGfiWQDu6DzA67Y+xHw1z9D8MKfyw5zO\nn+HxsSd5fOzJy/58I2aE/lQvWzJ9DGYG2JLpJxfLErOiRK3o+SyliIiIiMhNYtMEc5cTi1rEogk6\nLnFMsVzj5Gie4yPzHDs7z6lzecami6xXbjNqm3S2JOhqSQTL1mC9LTuIUYHxUhnPB8/zaXg+nu8T\njZhBkJi0iVgmpmHSkWijI9F2+Re8Gqmrn0Lwn+z5Pl7Wdz9/duxveH76CEdmjvHQllcQH4nwd0e/\nRN1vsDW7hbfueiO7WrY3z7u1Yz8QBKbjxUmG8meo1CuYholpGJiGhWUEdS03ypwpjHKmcJaRhVFO\nF84Aj19QlqhpE4vEiFmxICD2g96SPh744OFjAIOZgWYw3RZvfYE3S0RERETkpadg7iok4zYHdrRz\nYEf7iu2+7+P74Pk+nhesV2pBpq9QrJEPl0vPI7bFwmKFhudTb/jUGx6NhkehWGNirsTI5IXNFq9E\nImaRTgQZwFwqSl9Hiv7OFAMdaXrak9dlrr6+dA8/d8dP8PTkQT517NN8fvjLALTFW/m+nW/grq7b\nLpo1Mw2T3lQ3vVcYSNa9OucWxzlTGOFMYZTF2iKVRoVKo0qlUaHcqFCpVynVSxhL/wwDAzAMk5pX\n48mJZ3hy4hkAupId7G3dw9623Qyk+4haNhEzgm1GsAxLg82IiIiIyHWlYG4dGIaBYYCJAWG3rVjU\nIptae166S7UR9n2fQqnG5GyJibkSk7MlZgqV4PqmgWkYWOHSMKFa9SiUloLFGgulKqfHCjQ8n6eO\nTTWva5kGPW1J+jtTJGKRZtDp+UGGz/eh3vAoVxuUq/VgWQnWq3WPrtYE23uz7OjLsqM3S19HakVw\n2Cz3XInJuRKFxRoHdrTR257CMAzu7LqVW9odvnz2UVoyKe7M3Ym9aqTOFytiRtiS6WdLpv8Fne/7\nPuPFCQ7PHOPIzDGOzZ3gqyOP8dWRxy441sAgYkaIWVG6kh1h0NnTXGajaQV7Ijc4z/co1cvBYE/1\nIou1IpVGNfiCDv+SS2+t7au3hcexxjmxUYv8YpG616Du1c8//MbK1/G95jqGgYnRbKVgGAYmwf/D\nDb9B3WvQ8MOH51H36yu/tMIM14O/Wef3GSvXr2i5/HwT27SwzAgR0yJiRM5P/dN8vmw6IDNCey3D\nYqG66pjIsq4E4d8m/LAlhY+B0XyN5vRChoVlWmpKLyKbloK5G4xhGGSTUbLJKDv7X9icc77vUyjW\nGJlc4OzUIiOTC4xMLgbrU5fP+lmmQTxqEY9atKRjRCyTczOLjEwu8vVnzwEQjZgM9mTIJGwm58pM\nzpeoVBsrL/RF2L+tlYfuGuD2Xe1ErSiv3frqC4LZesPj5GiekckF0skoLekorekYuXQMO3Lt/kAb\nhhEMLJPq5tVbXk7Da3AqP8yRmWNMl2eoeXXqXo1aox6u1ynVSyumrFiSiiTpz/SxNTPAYHaArZkB\n2uKtCvBE1onv+9T9BpV65XzmvVFd8bxcr4Sj8xYpNkfqDdaXRupdPT3NzcA0TCwjCHggaE7uN5uW\nXxiE3gxMwwwDu/PBoWVazXlkV6/bRoSoFWuOJh2zosSa68H2mLXyeTwSv+zozCIi15r+V7oJGYZB\nNhUlm2pj37KBXjzfZyZfplb3MM3wW12D5je8lmWQiFpELPOCoKPheZybKnLyXJ5T5/KcGs1zciSP\n5/vEolazn19nS5zOlgS2ZfLYwTEODc1yaGiWtmyMV97Rz3ff3kdbm8eJ0XmOnJ7lyOlZjo3MU62t\nPU9fOmHTko6RSdrEoxaJWIRENEI8trRukUrYpOI2yXiEVMImHY8Qj0UwX2TgZJkWu1q2r+jPt5aa\nV2eiOMm5xXHOLYxxbnGc0cUxjs4e5+js8eZxKTvJYGYAp3s7CS9Fa7yF1lgLrfFccwJ6kc3K8z0W\na0Xy1QJzlTzzlXnmK3nmqnnmK3kK1YWwqfT5YO1K5vdcLWJYJO0k2ViGnlR3ONBTkqSdIBVJErNi\nzf8TL5W5MsMlq55fKqNlhksw6GjLUMhXmhkpO8xKWYYVvr55wWudz+55YdYqWAJYhollRpr9ia/G\nRTOQgL8sM+gvOzZ43WC94Xk0/HqQZVxarso2Buvh0q8TS1jkC0XqXp3aqnMMDFhq7bIsE7j0Wkuv\n0Vh23vLM5PLXKnvlFce+2OA1FUmSi2VpieVoiWXJhcu2eCvtiTba4q0K+ETkmto0UxPcSG6WoVgr\ntQbVWoN0OKLnWs5OLvCl74zw2PNjVKoNLNMgaluUKuenT+jvSLF3ayvbejIUK3XmFirMFSrMLVSZ\nLVSYXahcmPW7DMOAZCwSBnoRUnGbVCII+GIRi1rDo1b3qDeCR63u4Xk+rdk43a0JutuSdLcGAeoL\n7WtYqpc5UzjL6fxZhsPldHmN2ewJ5ghsjbdwa8d+Hh585U0/x97N8jvwQt2M9a95dSr1pQxZGHAt\nPV+2fWlbzawwXZijUFtkobrAYq14yQ/almEty6IsPaLNDMr5bEp0xfNUJBidNx2O0hs1L/7/1bV0\nM74Hrsb1qr/ne0EA6dWb78elvtUr3qvhcvl6sV5iPvyiodxYezojA4NcLEtHoo32eBudiQ760z30\np3svaJ2h94Dqv5nrD7oH6zU1gYK562AzvnlLlTqPHRzjq8+M4vmwuz/L3q2t7B1svWjfwuWa/fkq\ndUrVBqVKnXK1TrFcb04nUWxOJ3Hh9BL1xgt7n5uGQXsubPJpmUQsk4hlYFkmtmVgR0yyqSi5VIyW\ndIyWdJRcOni+VhPRhdoixUieU2OjzFbmmC3PMVuZZ7Y8x3R5hkqjSiKS4OHBV/KqLS8nZl3+3mxE\nm/F3YLkbrf6+77NYL5KvFKg0qs3syYoMR6PCQnWBQm2BQvX8Y6G2SKlepuFf3RcuS1KRJOlomrSd\nIhNNk42mycVyQfYjmg2zIFkSkcQNEYStlxvtPXCtbfT6l+vlIHNcyTNXmWemPMtUaYbp8gxTpRnm\nKvMXfDkRt+LNwK4v3cttW3aTrOc2bSZvo78HXqzNXn/QPVAwt4HpzXtt6780YfxSsFete0TCQCxi\nmc2lacB0vsL4TJHx2WBS+fHZEuMzRRaKtatunNOaidHXkaKvPUVfR5L+jjR9HUm2bmm7oP6+71Ou\nV3n03Df5h6EvsVgvkommecO21/CyvvsuO7/gRqPfgfWvv+d7KzIN5eXry/uV1SsU62XyYfPF+WqB\nfCVP/QWN+VvJAAAgAElEQVQEYyk7SdpOk4jEg4zZBX2Oouf7Ha3at62nm3LexzKtdb0PG4V+B27u\n+te9OjPlOSaKk4wsnGs+xouTK4I8y7DoT/cwGPavHsxsoS/VvSl+L27298DlbPb6g+6BJg0XuULL\nJ4xvzcQueWwybrOlK33Bdj8c9bNe96l7XjClRN2jWm+QX6wyt1BlbqHC/EKVucWgmej4bInnT83w\n/KmVTSszSbs5V2DD85vrAO3ZGDu2fB+N9hMcrz7FJ4/+FV8c/iqvGXwlA5k+OhPtpO3UTZWh2Kx8\n36dYL4UZrkKzuWGlUaXq1ag1asuWVWpenWqjSrVRo+at3FepV6h6tasug2mYZKMZ+jN95KJZsrEM\n8XAuxqURCG3TJmJaRK1omDnLkLbTpO3ki/rA2ZLIMLmwef+Iy80tYkboSnbQlezgQMe+5vZqo8bY\n4jhnF84xWR/HnTjFSGGU4cIIjD7ePHcg3cdgpr8Z5PUkuzZFgCciV0/BnMgVMAwDyzCwohBj5R/U\n3vbURc8rVeqMTi8yOrnI6HQwmuj8YhXP87FMI3yYwYA0wMjUIk88Pwu0QeTlJAeHmGk/zZ8e/cvm\nNWNmjM5kO13JDjoTwbQIA5k+uhId+mN/jXi+R7VRC/rZ1Ess1ossVBdZDEdMXKguUqwHw9wvBWVB\nEFal6tWpehXmy4UX3DTRNm2ipo1t2SSsOC2x3Nqj8IXZsPiKrFicRCRONpohZSc1pLvINRS17CAD\nlx1oZiWW5kgdzp/ldCHoYz1cOMtQfrh5nm3aDKT76EsHIy73JLvoSXXRGmvRl3sim5yCOZGXUCIW\nYWdfjp1956eZuFSzAs/3OTdd5OiZOY6dmcM9k2Xu7CBmbhIjXsSMFynFipypj3F2YXTFuSYWbXYn\nfaledrQOMJjrpTWeoyXWQnSd5/S7ESw1K6w0qjS8BnW/QWPZPFu1Rj2cO6zEYi0MtML5xKpebcU8\nYMtH7PNWjOjnNZ9XG9XmBPTVRu2qR8UzMLDNCFErSjIaZzDTTyaaIRNNBUs7TTqaIm7FgmDNslct\no9imjW1GNsyHN8/zqdYbVOse1VqDai0YbKhSa3B2psTk1AKVeoNazWse4y1r+r80imHwBHx/KUsO\nvnd+jszg5xX8/vgeze0QTLVihg9r2XL1dbzw2gB2JGh+bVsmkXDZ3Hax7cvW1xoRWORils+R+jLu\nB6DWqDG6ONYcQGu4cJbThTOcyq+cBidmRelOdtGZaCcXy5KNZpr9THPRYLTNeOTSLVJEZGNTMCdy\nAzENg/6OFP0dKV59Zz++7zM9X+bM5AJz4cies4UKM1NlZopzzNdnqdnzmMkCZrLAZGKcqdoYz849\nteK6USNO1s7SnmihI9VC1Io252GyDLO5DkGQ1PA9Gn4Dz/fC5w08L9ju+Y1wGWyPWTGyYfO7IDgJ\nBrGIR+JU6lVKjRLleplSvRIuy5gTHtP5PKV6OdjWCJY1rx7Oj3V+mPWl50ujyZXC+cFK9fK6z5HV\nHAp+jWHho6ZN3IqRi2aIRZaNpmjFSUdTwRD3keSK9VgkFmTQTJvIsiCsszPDxESeUqXOfNhMdz5f\nYbZUIx61yCSiWEmbRMImGbFJhlNteL5PtRY0763VzwdAF64HAVRtjWOr9SCgWn5ebdnxpmmsGaCY\nhkGl3qBabVBpBmcNKrVgJNilAMrzg6bDS+ub1VJ/3KWBkiIR6/z9tAysiMViqdr8WVTDn4Xn+SSi\nFom4TTJmkYxFlq3bJOIRkrFIuD1cxiLEoxaxqEXctrAjCiY3Otuy2Zrdwtbslua2WqPGRGmKscUJ\nxooTjC2OM7Y4wejiGMOFsxe9VkeinS2ZfgbDgHFLpp+0ffEWJSKysSiYE7mBGYZBR0uCjpa156Hz\nfZ/Fcp3p+TKTcyUm5hY5kx9jvDTGbHWGoreAES1TjpapRKeZqk7gzl/jSlyhpczVUiC5Ftu0SUYS\n5GJZelLdJCMJYla0OT9XxLTCADWYHDgZSTTnEVsKsJJ2kpgVXTX/19V9+PXDoGpp1NRiucZCKViO\nNEdXnQ2m76h71Jof1oMsVLnuMRvO+XhF98YAyzSpN65+XrUrYUdMomHQ5vlQWzw/dccFZQGitkXU\nNolGLDJJO8h4GQaGuWzuStMgEk5FElzbImYHy6ht0tqSoFapN/dHwyAkYhrhfGZAGKwH2bfguqYR\nZuyac2QSBuDL1k1Yyul5/sq+qQ0vCJggOM4Mzw3m3gxea2m6klrdWzGNSXNbuL2+bP/So3lcY+W2\ncqVOYdn2paA5Ft6bdMImGrEwTShVGhQrwe/1C/mZGwZBcGcH83BmEjbphE06GQ2WS9uS59dTiWAu\nTwWBNy7bsulP99Kf7l2x3fM98tUC85X8snka8+SreaZLs5wpjPDUxLM8NfFs85y2eCtdiQ6ysUyz\nv2wumiEXy5GxU+EXUVFiVlTN90VucArmRDYwwzCaH8629mTCrecnOa/VG4zPlIL+epMLnJ2ZY6ww\nQ6Veo9YIhp6v1hv4hgdG+KHRDybqxTeDdd/A91c+bz4wwGxg2BUMuwp2BStaxYrWMO06pm8TIUrE\niGIbMaJG8AEhGUtCzcI2Y0SNKLYZNC2MYBKLWsEH3KhJ1AY7YhCJGOBZ1GoEU1RUG5SLdcrVBg3P\nD5u9GUTMoPlbxDKxzCCTNe/5zDYHm1mk4S2s/IDf8Gn4fpB5XPahf/WytHwqjFKtOWjN1bDCD/Cp\nhE1/R4pcKkouHSOXitKSjpJORilX6yyUaiwUa8GyVKNQrNHwPKIRCzsMopaCr2gYHNnLAqKl4Ggp\niIpGzPPnhcvlAdzFPsAHg/4EAYjn+cTWMeuzmUcx832fzs4MU1MLlz22Vm9QrDQolmsUK3VKleB9\nWKzUKS0tK3Uq1QblWoNKrUGlGjxK1Tqz+Qojk4tXVK6IZZBK2NiWibU0DYtpNH+fErEImaRNJhkl\nm4yeX0/ZZBLB0o7og/+1ZhpmOIl5bs39vu8zU57jzMIIZ/JnGV4Y4UxhhCOzx67o+hEjGAApaNFh\nhV9+meEXIcEk9REjQsKOk4wkSEQSJCLBejwSb55jNs+xMA2DbCXBzFxh2WTvwaTznu9hYmCGLUeC\n1hnBNYLm9VWqXrU5IFTVC6ZTscJBm4IBnCwsM3iejWboTLTToQHE5CalYE7kJmZHLAa60gx0pWFf\n90WPqze8ZpO5aj3o21QNPxguNetbsV5d2Q9q9bZKfuk6DcrhtS4MfhpAMXzc+EzDIBkPJqLvyMWD\niejjkWBbuL40Mf3S85htYdvnA6+lCeg3SiBjGmFmzdYH9PUUZBav7AOlHbHIRSxyVzAf58U0PI/F\nUp1CqcZCsdr8kmDpi4LlzxdKNRphJrHe8Kh7Po2Gd8VzdcajQbY2CPaidLTE6Qubjvd1pEjFb77+\nuzc6wzBoT7TSnmjljs4Dze21Ri2YmqSaZ75SYL6aJ18pUKguBKPkhv2Eq+Gj0qhQ9xt4Xg3P9/B9\nP2hyTzARu+e/NC0H1lPMitKRaKcj3kZ7oi1YT7TTkWijLd66aef8k41N71oRCSdDN0nGX7rXWGp+\nVqk1yLUkmZxaWJH98jyfWsM7n1lYvqw1iEYs4tGlR9BHKB4LvuFteH6zOVs9/OBZb3grBrwwzWBE\nUnPZCKKrB8ew1ti21AQvqn5IskFZpkk2FSWbigIvrK+U7/uUqw0KxSr5Yo1CsUohXOYXl54H+/LF\nKkNjhTWz17lUlL6OFDsHWkjHI3S2xOkKm5LH9KXBNWVbNh2JNjoSbS/6Wr7vU/VqlOolirWgT3Op\nHvRvbvgeftgXe/kylY5TKdZXNZMP+kov9d32vEaz6X3Db2AaJjEz2swURq2gKWjEsJqDYNX9oNVJ\nzWtQ92rMV/JMlWaYKk0zVQ6WIwvnLqiDgUFLLBfek/aVy3g7KTupvwFyQ1IwJyLXxFLAmIhF6GxN\nYtRf2LD8InLtGUbQzDIRi9DVevnjl/rzTsyWGJla4NxUkZGpRUanFjl8epbDp2cvOCeXitLZkiCX\nji7r03d+PZuM0pqJkUna+lB9gzEMg1gYWF2suedq16uFgu/7LNQWg+CuNBM8ytNMl2aYLE1zfO4U\nx+ZOXnBe3IrTlWxnIN1Hf6YvWKZ7SURewm9BRa6AgjkRERFZV8v78+7oy67YV67WqfgGx05NMzFX\nCgZvmg2WJ0fzlx0FNWIZtGZitGbitGVjtGXi9LYn6e9M0deeUrNguSTDMMhE02Siabbntl6wv9ao\nMVOeZaocBHfTSwFfaZrRxfFggvdlib2ORDtb0n3c0r6X2ztvIWknr2FtRBTMiYiIyDUUj0bY0pkh\nF7sw6LpU/765hSpzhQozhTIzhQrHzsxdMDmJYUBXS4L+zjQDnSnas3Fi0aDfZ9y2muvJWIRcOoqp\nDJ+sYls23akuulNdF+xreA3Gi5OcXRjlbGG0uXxq8jmemnyOT7h/gdO2i7u6buf2jv0K7OSaUDAn\nIiIiN4Sr6d9Xb3jMLVSYni8zOl1kZHKBkclFzk4u8J2jk3zn6OQlz49FLQY6Us3Ab6AzGCwqndAg\nLbI2y7ToS/fQl+7hvp67gKDZ5kRpiqcnnuOpiWc5NO1yaNrlE4bF3rbd7G7ZQa45iXuGbDRDIrL2\ndEMiL4SCOREREdlwIpZJRy5BRy6BM3i+I5/v+8wvVjk7ucBcoRqOxHt+MKVKzWOxVGN0apGhsQIn\nRvMrrtuejbNrIMeu/uAx0JXCMs1rXT3ZIAzDoDvZyeu2PcTrtj3ERHGqOa/f89NHeH76yAXn2GaE\ntkQLXYkuBtK99If97zoSbZiG3mtydRTMiYiIyE3DMAxa0jFa0rHLHltveJybLnJ2WVbv5Giexw+N\n8/ihcQCitsmO3iy7BnLs29rGrv4cdkQfuGVtXcmOZmA3WZxmdHGMfLVAvpJfNRXEPM9NHeK5qUPN\nc2NWlP50L1sy/WzLDrItu4XORIcG/JFLUjAnIiIim1LEMtnSlWZLV7q5zfd9JmZLHB+Zbz7c4TmO\nDM/xmcdOE42Y7NnSwv5tbezf1spAV1p972RNncl2OpPta+/rzHBiZJSRwjnOLowyshAsh/JnODl/\nmq/wGADJSIKt2S1syw6yPTfIjtw2jaApKyiYExEREQkZhkF3W5LutiQvu7UXgGK5xrGz8xwamuXQ\n0AwHTwUPgGzS5pbt7dy+q50D29tJxvXRSq5MNpoh255hX/ue5rZao9YM6obywwzND3N45iiHZ44C\nwXx4WzJ97G7Zye7WHezMbSdpqw/eZqb/cUREREQuIRm3uX1XB7fv6gBgbqHCoaEZDg3N8vzQDN94\nfoxvPD+GZRrsHsg1j+1p02iGcnVsy2Z7buuKaRMK1QVOhxm7Y3MnOZ0/w3BhhC+e+SoGBv3pXnbk\ntrEtu4XtuUE1zdxkFMyJiIiIXIWWdIwHD/Ty4IFefN9neHyBZ05M8czxaY6ETTL/9JHjdLclucfp\n5B6ni8HutD5gywuSiaY50LGPAx37AKg2qpyaH+bY3AmOzZ1kaH6YswujfHUkOD4VSQZNM3ODbEn3\n0RpvoSWWI22n9B68CSmYExEREXmBDMNga0+GrT0Z3vyy7cwvVHj25DTPHJ/m4Klp/vYbp/nbb5ym\nsyXOPU4X9+ztYltP5noXWzawqBXFaduF07YLuLBp5qn5YQ7NuByacVecFzEjtESztMRztMRyDGYG\n2NmyjS3pfizzwnkfZWO4bDDnOI4J/C/gdqACvNt13ePL9v9T4F8D88BHXdf9yEtUVhEREZEbWi4d\n4xW39fGK2/qo1BocPDnNE0cmeObENH/3+DB/9/gw7dk4r7l/kHt2ddCe02AW8uJcrGnmUH6Ycwvj\nzFXnmavkmSvPM1eZ48TcED4+3x5/GoCoabMtO8jOlm3szG1ne26QuAZZ2TCuJDP3FiDuuu53OY7z\nAPCbwPcBOI7TAfwKcBcwB3zBcZwvuq479BKVV0RERGRDiNkWdztd3O10Ua01eP7UDN92J3jq2BR/\n+vmjfPLzR7l1ZzuvvKOP23a2az47WTeZaJpbO/Zza8f+C/Y1vAazlTlOzQ9zYn6IE3OnODZ3kqNz\nJ4BgkJWBTB87c9vY2bKdHbmttMRy17oKcoWuJJh7OfD3AK7rftNxnHuW7dsBPOO67gyA4zhPAA8A\nQ+tcThEREZENK2pb3Lmnkzv3dFKpNjgyMs+nv3aSZ09M8+yJaVozMV5xWy+vuK1P2Tp5SVmmRUei\nnY5EO/f23AlAsVbk5PzpZnB3On+GM4URvnz2UQA64m3sbNnO7tad7G3dRWu85XpWQZa5kmAuS9CE\ncknDcZyI67p14Bhwi+M43UAB+B7g6PoXU0REROTmEItavOa+rdy+vY3h8QJfeXqUbzw/xt88OsTf\nPDrE1p4Md+3u4M7dnfR3atAKeekl7eSKQVZqjRrDhRFOzJ/ixNwQJ+eHeHzsSR4fexKAzkQ7Tusu\nnLbd7GnZSTqaup7F39QM3/cveYDjOL8FfNN13U+Gz8+6rjuwbP+bgH8DTAPjwN+6rvvXl7jkpV9Q\nREREZJMpVep87ekRvvb0CM8dn6LhBR+XetqT3H9LLw8c6GH/9nZMU4GdXHue73FmfpSD4y7PTbgc\nnjhGqV5u7h/I9rKzbSs727ayq20bW1v6sS37OpZ4Q1iXX+YrCebeBrzJdd13hn3mPuC67hvCfRHg\nF4FfBqLA54G3uq47dYlL+pOThfUo+4bV2ZlhM98D1X9z1x90DzZ7/UH3QPXf3PWHS9+DYrnGsyen\neeroFM+dnKZcbQDQlo3x4IEeXnagl+4NPofdZn8PbPT6N7wGw4WzuLPHcWeOM1Q4Q7VRbe63DIu+\ndA/bs4Pc2rGfPa07iZgrGwRu9HvwYnV2ZtYlmLuSZpZ/CTzsOM5jBBHkuxzH+WEg7bruhx3HAfgO\nUAZ+8zKBnIiIiIhcQjJu88D+Hh7Y30Ot7nFkeJZvH5ngiSMTfOax03zmsdPs6s/x4K093Le3i2Rc\nGRC5tizTao6g+fpt34Pne4wtTnC6cJbh/BlO588ysjDKmcIIXx35BolInAPt+7mj6wD72/YQtaLX\nuwo3jctm5l4CyszpmwjVfxPXH3QPNnv9QfdA9d/c9YcXdg8qtQZPHZ3k0efOcWhoFh+wIyYHtrex\nf1sb+7e10tOW3BB97Db7e2Az1L/u1Tk1P8wzUwd5euIgs5U5AGzT5pZ2h/u23k631Ut3snNDvGfX\n27XMzImIiIjIdRazLR64pYcHbulhJl/mG8+P8djBMZ46NsVTx4KGUa2ZGPu3trJvWyv7trbRmold\n51LLZhUxI+xu3cHu1h28bdebGC6c5enJgzw9+Vy4PAhA2k6xs2U7O3Pb2NWynYF0nyYxvwoK5kRE\nREQ2mLZsnO/9rm1873dtY2KuxOGhGQ4NzXL49CyPHhzj0YNjAOwayHH/vm7u2dtFLqWmbXJ9GIbB\n1uwWtma38OYdr2e8OMG5+ghPnz3Cibkhnpk8yDNhcGebEXqSXfSme+hNddOXCpat8RZMQ3MxrqZg\nTkRERGQD62pJ0HVHP6+8ox/P9zk7scChoVmePTGFOzzH8bPz/MkXjrJvayv37evmbqeTlPrZyXVi\nGAY9qW5u7dzFnbm7AJguzYbTIARz3I0VJzizMLrivJgVpTe1FOB1B+vpbnLR7KZsprlEwZyIiIjI\nTcI0DAa7Mwx2Z3j9/YPMLVR44vAE3zo8zqGhWQ4NzfJHn3MZ6EzTlo3Rlo3Tno031ztbEsrgyTXX\nnmilPdHKfT1BcOf5HlOlGc4tjjG6MM65xTHOLY5zpjDCUH54xbmJSIJt2S3c13MXd3Qe2HSDqyiY\nExEREblJtaRjPHzvFh6+dwuTcyW+dXicbx+ZZHR6kdPjaw/A0d2WZO9gC85gC86WVvW7k2vONEy6\nkh10JTu4vfNAc3vDazBRmuLc4jijC2NBsLc4xuGZoxyeOcqfWjHu6rqdB3rvYUdu66bI2CmYExER\nEdkEOlsSzX52vu+zUKoxk68wky8znS8zU6gwOrXI0TNzfOXpUb7ydNDMbSm46+9I0ZFL0J4LsnnJ\n+LX/GFmu1jk7EQSiw+MFhscXmJovkYrbpJM26YRNJmmTSURJJ236OlLs6s+RTqhZ6c3AMi16U930\nprq5q+u25vaJ4iSPn3uSb449yWPnvsVj575FV6KD+3ruxmnbxUC696bN2CmYExEREdlkDMMgk4yS\nSUbZ2pNZsa/heQyPL3BkeBZ3eK4Z3K2WjEVoz8XpCIO75nr4PJ2wMQyDhudRqXpUag2qtQaVWoPp\nYo2pqQXqDZ96w6Pe8Gl4HrW6R7naoFSpU6rWKVcalKp1SuU647MlxmeKLJ9UK2KZdOTilKp1psfK\nNLy1p9zq70ixayDH7oEcuwda6MjFN0XWZrPoSnbypp2v53t3vJajsyf45rlv8/Tkc3zm1Of4zKnP\nYWDQm+pmMDPAlmx/sEz3YVsbP8hXMCc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JkiRJorRSd9bEYZw1cRgfvmY6y1e/zHeefInv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\nGeYkSZIkKUP/H+sPLmVxWxHSAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11b7bb978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "table.plot(title='Sum of table1000.prop by year and sex',\n",
    "           yticks=np.linspace(0, 1.2, 13), xticks=range(1880, 2020, 10),\n",
    "           figsize=(15, 8))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "从图中可以看出，名字的多样性确实出现了增长（前1000项的比例降低）。另一个办法是计算占总出生人数前50%的不同名字的数量，这个数字不太好计算。我们只考虑2010年男孩的名字："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = boys[boys.year == 2010]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>sex</th>\n",
       "      <th>births</th>\n",
       "      <th>year</th>\n",
       "      <th>prop</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>260877</th>\n",
       "      <td>Jacob</td>\n",
       "      <td>M</td>\n",
       "      <td>21875</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.011523</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>260878</th>\n",
       "      <td>Ethan</td>\n",
       "      <td>M</td>\n",
       "      <td>17866</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.009411</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>260879</th>\n",
       "      <td>Michael</td>\n",
       "      <td>M</td>\n",
       "      <td>17133</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.009025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>260880</th>\n",
       "      <td>Jayden</td>\n",
       "      <td>M</td>\n",
       "      <td>17030</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.008971</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>260881</th>\n",
       "      <td>William</td>\n",
       "      <td>M</td>\n",
       "      <td>16870</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.008887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>261872</th>\n",
       "      <td>Camilo</td>\n",
       "      <td>M</td>\n",
       "      <td>194</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>261873</th>\n",
       "      <td>Destin</td>\n",
       "      <td>M</td>\n",
       "      <td>194</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>261874</th>\n",
       "      <td>Jaquan</td>\n",
       "      <td>M</td>\n",
       "      <td>194</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>261875</th>\n",
       "      <td>Jaydan</td>\n",
       "      <td>M</td>\n",
       "      <td>194</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>261876</th>\n",
       "      <td>Maxton</td>\n",
       "      <td>M</td>\n",
       "      <td>193</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000102</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           name sex  births  year      prop\n",
       "260877    Jacob   M   21875  2010  0.011523\n",
       "260878    Ethan   M   17866  2010  0.009411\n",
       "260879  Michael   M   17133  2010  0.009025\n",
       "260880   Jayden   M   17030  2010  0.008971\n",
       "260881  William   M   16870  2010  0.008887\n",
       "...         ...  ..     ...   ...       ...\n",
       "261872   Camilo   M     194  2010  0.000102\n",
       "261873   Destin   M     194  2010  0.000102\n",
       "261874   Jaquan   M     194  2010  0.000102\n",
       "261875   Jaydan   M     194  2010  0.000102\n",
       "261876   Maxton   M     193  2010  0.000102\n",
       "\n",
       "[1000 rows x 5 columns]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "对prop降序排列后，我们想知道前面多少个名字的人数加起来才够50%。虽然编写一个for循环也能达到目的，但NumPy有一种更聪明的矢量方式。先计算prop的累计和cumsum，，然后再通过searchsorted方法找出0.5应该被插入在哪个位置才能保证不破坏顺序："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "prop_cumsum = df.sort_values(by='prop', ascending=False).prop.cumsum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "260877    0.011523\n",
       "260878    0.020934\n",
       "260879    0.029959\n",
       "260880    0.038930\n",
       "260881    0.047817\n",
       "260882    0.056579\n",
       "260883    0.065155\n",
       "260884    0.073414\n",
       "260885    0.081528\n",
       "260886    0.089621\n",
       "Name: prop, dtype: float64"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prop_cumsum[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([116])"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prop_cumsum.searchsorted(0.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "由于数组索引是从0开始的，因此我们要给这个结果加1，即最终结果为117。拿1900年的数据来做个比较，这个数字要小得多："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "41853    0.979223\n",
       "41852    0.979277\n",
       "41851    0.979330\n",
       "41850    0.979383\n",
       "41849    0.979436\n",
       "41848    0.979489\n",
       "41847    0.979542\n",
       "41846    0.979595\n",
       "41845    0.979648\n",
       "41876    0.979702\n",
       "Name: prop, dtype: float64"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = boys[boys.year == 1900]\n",
    "in1900 = df.sort_values(by='prop', ascending=False).prop.cumsum()\n",
    "in1900[-10:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([25])"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "in1900.searchsorted(0.5) + 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "现在就可以对所有year/sex组合执行这个计算了。按这两个字段进行groupby处理，然后用一个函数计算各分组的这个值："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def get_quantile_count(group, q=0.5):\n",
    "    group = group.sort_values(by='prop', ascending=False)\n",
    "    return group.prop.cumsum().searchsorted(q) + 1\n",
    "\n",
    "diversity = top1000.groupby(['year', 'sex']).apply(get_quantile_count)\n",
    "diversity = diversity.unstack('sex')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "现在，这个diversity有两个时间序列（每个性别各一个，按年度索引）。通过IPython，可以看到其内容，还可以绘制图标"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>sex</th>\n",
       "      <th>F</th>\n",
       "      <th>M</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1880</th>\n",
       "      <td>[38]</td>\n",
       "      <td>[14]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1881</th>\n",
       "      <td>[38]</td>\n",
       "      <td>[14]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1882</th>\n",
       "      <td>[38]</td>\n",
       "      <td>[15]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1883</th>\n",
       "      <td>[39]</td>\n",
       "      <td>[15]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1884</th>\n",
       "      <td>[39]</td>\n",
       "      <td>[16]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "sex      F     M\n",
       "year            \n",
       "1880  [38]  [14]\n",
       "1881  [38]  [14]\n",
       "1882  [38]  [15]\n",
       "1883  [39]  [15]\n",
       "1884  [39]  [16]"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diversity.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "可以看到上面表格中的值为list，如果不加diversity=diversity.astype(float)的话，会报错显示，“no numeric data to plot” error。通过加上这句来更改数据类型，就能正常绘图了："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>sex</th>\n",
       "      <th>F</th>\n",
       "      <th>M</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1880</th>\n",
       "      <td>38.0</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1881</th>\n",
       "      <td>38.0</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1882</th>\n",
       "      <td>38.0</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1883</th>\n",
       "      <td>39.0</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1884</th>\n",
       "      <td>39.0</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006</th>\n",
       "      <td>209.0</td>\n",
       "      <td>99.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007</th>\n",
       "      <td>223.0</td>\n",
       "      <td>103.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008</th>\n",
       "      <td>234.0</td>\n",
       "      <td>109.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009</th>\n",
       "      <td>241.0</td>\n",
       "      <td>114.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010</th>\n",
       "      <td>246.0</td>\n",
       "      <td>117.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>131 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "sex       F      M\n",
       "year              \n",
       "1880   38.0   14.0\n",
       "1881   38.0   14.0\n",
       "1882   38.0   15.0\n",
       "1883   39.0   15.0\n",
       "1884   39.0   16.0\n",
       "...     ...    ...\n",
       "2006  209.0   99.0\n",
       "2007  223.0  103.0\n",
       "2008  234.0  109.0\n",
       "2009  241.0  114.0\n",
       "2010  246.0  117.0\n",
       "\n",
       "[131 rows x 2 columns]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diversity = diversity.astype('float')\n",
    "diversity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11b3b7eb8>"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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GnCT1HbOsBUHwPeBaoCr51lTgx2EY3tFkmb7ArcA0IA94PQiCF8MwrGn9yJIk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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11b31e198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "diversity.plot(title='Number of popular names in top 50%', figsize=(15, 8))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "从图中可以看出，女孩名字的多样性总是比男孩高，而且还变得越来越高。我们可以自己分析一下具体是什么在驱动这个多样性（比如拼写形式的变化）。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## “最后一个字母”的变革\n",
    "\n",
    "一位研究人员指出：近百年来，男孩名字在最后一个字母上的分布发生了显著的变化。为了了解具体的情况，我们首先将全部出生数据在年度、性别以及末字母上进行了聚合："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 从name列中取出最后一个字母\n",
    "get_last_letter = lambda x: x[-1]\n",
    "last_letters = names.name.map(get_last_letter)\n",
    "last_letters.name = 'last_letter'\n",
    "\n",
    "table = names.pivot_table('births', index=last_letters,\n",
    "                          columns=['sex', 'year'], aggfunc=sum)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.series.Series'>\n",
      "0    y\n",
      "1    a\n",
      "2    a\n",
      "3    h\n",
      "4    e\n",
      "Name: last_letter, dtype: object\n"
     ]
    }
   ],
   "source": [
    "print(type(last_letters))\n",
    "print(last_letters[:5])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "然后，我们选出具有一个代表性的三年，并输出前几行："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th>sex</th>\n",
       "      <th colspan=\"3\" halign=\"left\">F</th>\n",
       "      <th colspan=\"3\" halign=\"left\">M</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th>1910</th>\n",
       "      <th>1960</th>\n",
       "      <th>2010</th>\n",
       "      <th>1910</th>\n",
       "      <th>1960</th>\n",
       "      <th>2010</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>last_letter</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>108376.0</td>\n",
       "      <td>691247.0</td>\n",
       "      <td>670605.0</td>\n",
       "      <td>977.0</td>\n",
       "      <td>5204.0</td>\n",
       "      <td>28438.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>NaN</td>\n",
       "      <td>694.0</td>\n",
       "      <td>450.0</td>\n",
       "      <td>411.0</td>\n",
       "      <td>3912.0</td>\n",
       "      <td>38859.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>5.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>946.0</td>\n",
       "      <td>482.0</td>\n",
       "      <td>15476.0</td>\n",
       "      <td>23125.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>6750.0</td>\n",
       "      <td>3729.0</td>\n",
       "      <td>2607.0</td>\n",
       "      <td>22111.0</td>\n",
       "      <td>262112.0</td>\n",
       "      <td>44398.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>e</th>\n",
       "      <td>133569.0</td>\n",
       "      <td>435013.0</td>\n",
       "      <td>313833.0</td>\n",
       "      <td>28655.0</td>\n",
       "      <td>178823.0</td>\n",
       "      <td>129012.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "sex                 F                            M                    \n",
       "year             1910      1960      2010     1910      1960      2010\n",
       "last_letter                                                           \n",
       "a            108376.0  691247.0  670605.0    977.0    5204.0   28438.0\n",
       "b                 NaN     694.0     450.0    411.0    3912.0   38859.0\n",
       "c                 5.0      49.0     946.0    482.0   15476.0   23125.0\n",
       "d              6750.0    3729.0    2607.0  22111.0  262112.0   44398.0\n",
       "e            133569.0  435013.0  313833.0  28655.0  178823.0  129012.0"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "subtable = table.reindex(columns=[1910, 1960, 2010], level='year')\n",
    "subtable.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "接下来我们需要安总出生数对该表进行规范化处理，一遍计算出个性别各末字母站总出生人数的比例："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sex  year\n",
       "F    1910     396416.0\n",
       "     1960    2022062.0\n",
       "     2010    1759010.0\n",
       "M    1910     194198.0\n",
       "     1960    2132588.0\n",
       "     2010    1898382.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "subtable.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "    .dataframe thead th {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th>sex</th>\n",
       "      <th colspan=\"3\" halign=\"left\">F</th>\n",
       "      <th colspan=\"3\" halign=\"left\">M</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th>1910</th>\n",
       "      <th>1960</th>\n",
       "      <th>2010</th>\n",
       "      <th>1910</th>\n",
       "      <th>1960</th>\n",
       "      <th>2010</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>last_letter</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>0.273390</td>\n",
       "      <td>0.341853</td>\n",
       "      <td>0.381240</td>\n",
       "      <td>0.005031</td>\n",
       "      <td>0.002440</td>\n",
       "      <td>0.014980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000343</td>\n",
       "      <td>0.000256</td>\n",
       "      <td>0.002116</td>\n",
       "      <td>0.001834</td>\n",
       "      <td>0.020470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>0.000013</td>\n",
       "      <td>0.000024</td>\n",
       "      <td>0.000538</td>\n",
       "      <td>0.002482</td>\n",
       "      <td>0.007257</td>\n",
       "      <td>0.012181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>0.017028</td>\n",
       "      <td>0.001844</td>\n",
       "      <td>0.001482</td>\n",
       "      <td>0.113858</td>\n",
       "      <td>0.122908</td>\n",
       "      <td>0.023387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>e</th>\n",
       "      <td>0.336941</td>\n",
       "      <td>0.215133</td>\n",
       "      <td>0.178415</td>\n",
       "      <td>0.147556</td>\n",
       "      <td>0.083853</td>\n",
       "      <td>0.067959</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>v</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000060</td>\n",
       "      <td>0.000117</td>\n",
       "      <td>0.000113</td>\n",
       "      <td>0.000037</td>\n",
       "      <td>0.001434</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>w</th>\n",
       "      <td>0.000020</td>\n",
       "      <td>0.000031</td>\n",
       "      <td>0.001182</td>\n",
       "      <td>0.006329</td>\n",
       "      <td>0.007711</td>\n",
       "      <td>0.016148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>x</th>\n",
       "      <td>0.000015</td>\n",
       "      <td>0.000037</td>\n",
       "      <td>0.000727</td>\n",
       "      <td>0.003965</td>\n",
       "      <td>0.001851</td>\n",
       "      <td>0.008614</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>y</th>\n",
       "      <td>0.110972</td>\n",
       "      <td>0.152569</td>\n",
       "      <td>0.116828</td>\n",
       "      <td>0.077349</td>\n",
       "      <td>0.160987</td>\n",
       "      <td>0.058168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>z</th>\n",
       "      <td>0.002439</td>\n",
       "      <td>0.000659</td>\n",
       "      <td>0.000704</td>\n",
       "      <td>0.000170</td>\n",
       "      <td>0.000184</td>\n",
       "      <td>0.001831</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>26 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "sex                 F                             M                    \n",
       "year             1910      1960      2010      1910      1960      2010\n",
       "last_letter                                                            \n",
       "a            0.273390  0.341853  0.381240  0.005031  0.002440  0.014980\n",
       "b                 NaN  0.000343  0.000256  0.002116  0.001834  0.020470\n",
       "c            0.000013  0.000024  0.000538  0.002482  0.007257  0.012181\n",
       "d            0.017028  0.001844  0.001482  0.113858  0.122908  0.023387\n",
       "e            0.336941  0.215133  0.178415  0.147556  0.083853  0.067959\n",
       "...               ...       ...       ...       ...       ...       ...\n",
       "v                 NaN  0.000060  0.000117  0.000113  0.000037  0.001434\n",
       "w            0.000020  0.000031  0.001182  0.006329  0.007711  0.016148\n",
       "x            0.000015  0.000037  0.000727  0.003965  0.001851  0.008614\n",
       "y            0.110972  0.152569  0.116828  0.077349  0.160987  0.058168\n",
       "z            0.002439  0.000659  0.000704  0.000170  0.000184  0.001831\n",
       "\n",
       "[26 rows x 6 columns]"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "letter_prop = subtable / subtable.sum()\n",
    "letter_prop"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "有了这个字母比例数据后，就可以生成一张各年度各性别的条形图了："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11bb53b00>"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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HHgXmZeYDEfEAMCcztzZR+wJgbmb+bkTcAnwzM5fubb1azc8Ct2bmVyPiCOCa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\nRZkxypEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11b743ba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(2, 1, figsize=(10, 8))\n",
    "letter_prop['M'].plot(kind='bar', rot=0, ax=axes[0], title='Male')\n",
    "letter_prop['F'].plot(kind='bar', rot=0, ax=axes[1], title='Femal', legend=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "从上图可以看出来，从20世纪60年代开始，以字母'n'结尾的男孩名字出现了显著的增长。回到之前创建的那个完整表，按年度和性别对其进行规范化处理，并在男孩名字中选取几个字母，最后进行转置以便将各个列做成一个时间序列："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th>sex</th>\n",
       "      <th colspan=\"10\" halign=\"left\">F</th>\n",
       "      <th>...</th>\n",
       "      <th colspan=\"10\" halign=\"left\">M</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th>1880</th>\n",
       "      <th>1881</th>\n",
       "      <th>1882</th>\n",
       "      <th>1883</th>\n",
       "      <th>1884</th>\n",
       "      <th>1885</th>\n",
       "      <th>1886</th>\n",
       "      <th>1887</th>\n",
       "      <th>1888</th>\n",
       "      <th>1889</th>\n",
       "      <th>...</th>\n",
       "      <th>2001</th>\n",
       "      <th>2002</th>\n",
       "      <th>2003</th>\n",
       "      <th>2004</th>\n",
       "      <th>2005</th>\n",
       "      <th>2006</th>\n",
       "      <th>2007</th>\n",
       "      <th>2008</th>\n",
       "      <th>2009</th>\n",
       "      <th>2010</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>last_letter</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>0.345587</td>\n",
       "      <td>0.343440</td>\n",
       "      <td>0.338764</td>\n",
       "      <td>0.341251</td>\n",
       "      <td>0.338550</td>\n",
       "      <td>0.341270</td>\n",
       "      <td>0.339703</td>\n",
       "      <td>0.335258</td>\n",
       "      <td>0.332764</td>\n",
       "      <td>0.328706</td>\n",
       "      <td>...</td>\n",
       "      <td>0.020162</td>\n",
       "      <td>0.020019</td>\n",
       "      <td>0.019177</td>\n",
       "      <td>0.019505</td>\n",
       "      <td>0.018481</td>\n",
       "      <td>0.017635</td>\n",
       "      <td>0.016747</td>\n",
       "      <td>0.016189</td>\n",
       "      <td>0.015927</td>\n",
       "      <td>0.014980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.026256</td>\n",
       "      <td>0.025418</td>\n",
       "      <td>0.024368</td>\n",
       "      <td>0.023171</td>\n",
       "      <td>0.021645</td>\n",
       "      <td>0.020778</td>\n",
       "      <td>0.020357</td>\n",
       "      <td>0.019655</td>\n",
       "      <td>0.019693</td>\n",
       "      <td>0.020470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000046</td>\n",
       "      <td>0.000045</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.013972</td>\n",
       "      <td>0.014048</td>\n",
       "      <td>0.014042</td>\n",
       "      <td>0.013514</td>\n",
       "      <td>0.013083</td>\n",
       "      <td>0.012991</td>\n",
       "      <td>0.012983</td>\n",
       "      <td>0.012458</td>\n",
       "      <td>0.012186</td>\n",
       "      <td>0.012181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>0.006693</td>\n",
       "      <td>0.006601</td>\n",
       "      <td>0.006806</td>\n",
       "      <td>0.007211</td>\n",
       "      <td>0.007100</td>\n",
       "      <td>0.006478</td>\n",
       "      <td>0.006967</td>\n",
       "      <td>0.007035</td>\n",
       "      <td>0.007266</td>\n",
       "      <td>0.007703</td>\n",
       "      <td>...</td>\n",
       "      <td>0.031352</td>\n",
       "      <td>0.028794</td>\n",
       "      <td>0.027069</td>\n",
       "      <td>0.026118</td>\n",
       "      <td>0.025420</td>\n",
       "      <td>0.025075</td>\n",
       "      <td>0.024451</td>\n",
       "      <td>0.023574</td>\n",
       "      <td>0.023398</td>\n",
       "      <td>0.023387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>e</th>\n",
       "      <td>0.366819</td>\n",
       "      <td>0.370616</td>\n",
       "      <td>0.374582</td>\n",
       "      <td>0.373159</td>\n",
       "      <td>0.372722</td>\n",
       "      <td>0.372896</td>\n",
       "      <td>0.372802</td>\n",
       "      <td>0.372324</td>\n",
       "      <td>0.373675</td>\n",
       "      <td>0.373736</td>\n",
       "      <td>...</td>\n",
       "      <td>0.074927</td>\n",
       "      <td>0.074603</td>\n",
       "      <td>0.073396</td>\n",
       "      <td>0.071710</td>\n",
       "      <td>0.070799</td>\n",
       "      <td>0.069748</td>\n",
       "      <td>0.069445</td>\n",
       "      <td>0.069362</td>\n",
       "      <td>0.068663</td>\n",
       "      <td>0.067959</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 262 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "sex                 F                                                    \\\n",
       "year             1880      1881      1882      1883      1884      1885   \n",
       "last_letter                                                               \n",
       "a            0.345587  0.343440  0.338764  0.341251  0.338550  0.341270   \n",
       "b                 NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "c                 NaN       NaN  0.000046  0.000045       NaN       NaN   \n",
       "d            0.006693  0.006601  0.006806  0.007211  0.007100  0.006478   \n",
       "e            0.366819  0.370616  0.374582  0.373159  0.372722  0.372896   \n",
       "\n",
       "sex                                                    ...            M  \\\n",
       "year             1886      1887      1888      1889    ...         2001   \n",
       "last_letter                                            ...                \n",
       "a            0.339703  0.335258  0.332764  0.328706    ...     0.020162   \n",
       "b                 NaN       NaN       NaN       NaN    ...     0.026256   \n",
       "c                 NaN       NaN       NaN       NaN    ...     0.013972   \n",
       "d            0.006967  0.007035  0.007266  0.007703    ...     0.031352   \n",
       "e            0.372802  0.372324  0.373675  0.373736    ...     0.074927   \n",
       "\n",
       "sex                                                                      \\\n",
       "year             2002      2003      2004      2005      2006      2007   \n",
       "last_letter                                                               \n",
       "a            0.020019  0.019177  0.019505  0.018481  0.017635  0.016747   \n",
       "b            0.025418  0.024368  0.023171  0.021645  0.020778  0.020357   \n",
       "c            0.014048  0.014042  0.013514  0.013083  0.012991  0.012983   \n",
       "d            0.028794  0.027069  0.026118  0.025420  0.025075  0.024451   \n",
       "e            0.074603  0.073396  0.071710  0.070799  0.069748  0.069445   \n",
       "\n",
       "sex                                        \n",
       "year             2008      2009      2010  \n",
       "last_letter                                \n",
       "a            0.016189  0.015927  0.014980  \n",
       "b            0.019655  0.019693  0.020470  \n",
       "c            0.012458  0.012186  0.012181  \n",
       "d            0.023574  0.023398  0.023387  \n",
       "e            0.069362  0.068663  0.067959  \n",
       "\n",
       "[5 rows x 262 columns]"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "letter_prop = table / table.sum()\n",
    "letter_prop.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>last_letter</th>\n",
       "      <th>d</th>\n",
       "      <th>n</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1880</th>\n",
       "      <td>0.083055</td>\n",
       "      <td>0.153213</td>\n",
       "      <td>0.075760</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1881</th>\n",
       "      <td>0.083247</td>\n",
       "      <td>0.153214</td>\n",
       "      <td>0.077451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1882</th>\n",
       "      <td>0.085340</td>\n",
       "      <td>0.149560</td>\n",
       "      <td>0.077537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1883</th>\n",
       "      <td>0.084066</td>\n",
       "      <td>0.151646</td>\n",
       "      <td>0.079144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1884</th>\n",
       "      <td>0.086120</td>\n",
       "      <td>0.149915</td>\n",
       "      <td>0.080405</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "last_letter         d         n         y\n",
       "year                                     \n",
       "1880         0.083055  0.153213  0.075760\n",
       "1881         0.083247  0.153214  0.077451\n",
       "1882         0.085340  0.149560  0.077537\n",
       "1883         0.084066  0.151646  0.079144\n",
       "1884         0.086120  0.149915  0.080405"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dny_ts = letter_prop.loc[['d', 'n', 'y'], 'M'].T\n",
    "dny_ts.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "有了这个时间序列的DataFrame后，就可以通过其plot方法绘制出一张趋势图："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11bbb0390>"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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HxStp93QwM2MaIUF8m5szUbgSERGRXlPf0cCHxSuJDY1hdlbfmDT0VApXIiIi0mvePPAe\nbp+b64ZcRbgz3OpyesQ5x+IMw7ADTwL5QCfwoGma+09YfhPw74AfWGya5m/OtY6IiIj0P4cai9h4\nZCtZMRlcmjbB6nJ6TFdGrq4Hwk3TnMrREPXLzxYYhuEAfgJcAUwFHjEMY8DZ1hEREZH+x+f38cq+\nJQDcPOw67La+e/CsK+9sBrAUwDTN9cDEzxaYpukFRpqm2QgkAQ7AdbZ1REREpP/ZdGQbh5uKuSRl\nLLnxg60up0d15RT9WKDxhJ+9hmE4TdP0AJim6TEM40bg98A7QOu51jmT5OSY8ypeukZ97Rnqa/dT\nT3uG+toz1Neu6/B0smTdUkLsTu6ffCvJUWfuXV/oa1fCVRNw4ju1nxqSTNN8zTCMN4BngHu6ss7p\nVFc3d6EcOR/JyTHqaw9QX7ufetoz1Neeob6enzf2v0tdewNX5czB1hZKddvpexdMfT1bCOzKYcE1\nwHwAwzCmAAWfLTAMI9YwjJWGYYSZpunj6KiV72zriIiISP+xq3YPHxavYEBEEnNzZltdTq/oysjV\n68BcwzDWAjbgPsMw7gSiTdN8yjCMxcAqwzDcwA7geY5eOXjSOj1TvoiIiASq+o4GFu1+AafNwYOj\nv0y4M8zqknrFOcPVsRGph095eM8Jy58CnjrNqqeuIyIiIv2E1+flr7v+Tqu7jduG30BWTIbVJfWa\nvnsdpIiIiFjm7UMfcLDxMJekjOVLGVOsLqdXKVyJiIhIt9pVu4cPipYzICKJO0fcjM1ms7qkXqVw\nJSIiIt1mX/0B/rLz+ePnWUX00VvcnE3fuxW1iIiIWGJX7R6eLngWn9/P/aPu7FfnWZ1I4UpEREQu\n2paqHTyz6x/YbTa+NvYrjEoyrC7JMgpXIiIiclHWV2zi+cKXCXOE8vDY+xiWMMTqkiylcCUiIiIX\nzKzbz/OFLxPhDOfRcQ+SE5tldUmW0wntIiIickFa3W08W/giNpuNR/IfULA6RuFKREREzpvf7+cf\ne16lobORBYPnMTgu2+qSAobClYiIiJy39ZWb2VpdwNC4wczLmWV1OQFF4UpERETOS3VbLS/vfYNw\nRzj35t2O3aY4cSJ1Q0RERLrM6/OyaPcLdHpd3GZcT1JEgtUlBRyFKxEREemyj0tWcaipiIkDxzE5\n9RKrywlIClciIiLSJXUd9bx36COiQ6K4bfj1VpcTsBSuREREpEte2bcEl8/NjbnXEBkSaXU5AUvh\nSkRERM5pZ00h26t3MjRusA4HnoPClYiIiJyVy+vm5b1vYrfZuc24HpvNZnVJAU3hSkRERM7qw6Ll\n1HTUMTtzBhnRaVaXE/AUrkREROSMqtpq+KB4BXGhscwffIXV5QQFhSsRERE5o9f2v43H5+GmYdcS\n7gy3upygoHAlIiIip1XUVEJBzW6Gxg3ikpSxVpcTNBSuRERE5LTePfQhAAsGz9NJ7OdB4UpERES+\n4HBTMTtr95AbP5jhCUOtLieoKFyJiIjIF7yjUasLpnAlIiIiJznUWMTuWpNh8UM0anUBFK5ERETk\nJJ+PWs21uJLgpHAlIiIixx1sLKKwbi/D44cyTKNWF0ThSkRERADo8HTw1oH3AFgwZJ7F1QQvp9UF\niIiIiLW8Pi9ryj/l3UMf0exuIS/RIDd+sNVlBS2FKxERkX5sW1UBbx54j6r2GsIcoSwYPJfLs2da\nXVZQU7gSERHpp7ZX7+Tpnc9ht9m5LGMaVw++nNjQGKvLCnoKVyIiIv3UR8WrAPjXCY+SHZtpcTV9\nh05oFxER6YeKmko42HiYvCRDwaqbKVyJiIj0Q8tL1gAwJ/NLFlfS9yhciYiI9DONnU1sqdpOamQK\nIxKHWV1On6NwJSIi0s98UrYer9/LrKzpum9gD1C4EhER6UfcXjeflK0jwhnB5NQJVpfTJylciYiI\n9CObqrbT4m5levpkwhyhVpfTJylciYiI9BN+v58VJauxYeOyjGlWl9NnKVyJiIj0E/sbDlHaUs64\n5NEkRSRYXU6fpXAlIiLSD/j9ft47/BEAs7JmWFxN36ZwJSIi0g9sr96JWb+fvCSDoXGDrC6nT1O4\nEhER6eNcXhev7n8bh83BzbnXavqFHqZwJSIi0sd9WLySuo56ZmfNYGBUitXl9HkKVyIiIn1YbXs9\nHxYtJzY0hqsHXW51Of2CwpWIiEgf9tr+t3H7PFw/dD7hznCry+kXFK5ERET6qD11+9hWXcDg2Bwm\npY63upx+Q+FKRESkD6rrqOcfe17Fho1bhy/EbtOv/N7itLoAERER6V6VrUf43bY/09DZyNWDLic7\nNtPqkvoVhSsREZE+5HBTMU9u/yut7jauHzqfuTmzrC6p3zlnuDIMww48CeQDncCDpmnuP2H5HcDj\ngAcoAB4xTdNnGMYWoOnY0w6ZpnlfdxcvIiIinyus28tTBc/i9rq5a8QtTEufZHVJ/VJXRq6uB8JN\n05xqGMYU4JfAQgDDMCKAHwNjTNNsMwzjH8A1hmF8ANhM05zVQ3WLiIgEBb/fz+46E7fPw+ikETjt\n3XfQqKGzkf31B9nXcJD9DYeobKvCaXfy0Ji7yU8e3W3bkfPTlb/hGcBSANM01xuGMfGEZZ3ANNM0\n2054vQ6OjnJFHgtZTuC7pmmu776yRUREAl+Lq5V/mK+yrXonANEhUVyaOoHp6ZMveDJPv9/PvoYD\nfFC0gsK6vccfD3WEMjJxOFcNupzc+MHdUr9cGJvf7z/rEwzD+DPwqmma7x37uRgYYpqm55TnfROY\nf+y/0cAU4M/AMOA9wDh1nVOcvRAREZEgsq1iN09uWERDRxMjk4cxJCGbVYfX0+xqBSArNo0wZxh2\nmx2H3Y7T7mRgdDJZsWlkxqWRFZtGZGgkHPs97Qe2V+7mjcL32V93GICRyblMSB9DXvJwBiVk4bQ7\nLHq3/dIZ7yHUlZGrJiDmhJ/tJ4akY+dk/QwYDtxkmqbfMIy9wH7TNP3AXsMwaoE0oORsG6qubu5C\nOXI+kpNj1NceoL52P/W0Z6ivPePUvtZ3NNDkaqbZ1UKzq4WDjUWsrdiAw+bg+qHzuTz7Muw2O3PT\nL2dH9S7Wlm/gYFMRPr8Pn9+H3+/Hj5+CI3u6tP385NHMzZ7F4Ljsow/4oL627ewrBYFg2l+Tk2PO\nuKwr4WoNcC3w0rFzrgpOWf4njh4evN40Td+xx+4HxgCPGIaRDsQCFedZt4iISEDz+X28YL7GmvIN\nX1iWGpnCV0bdQVZMxvHHQuxOJgzMZ8LA/C883+11U9VeQ0VLJRWtR6hoq8LldQFgOzZIkhgez+ys\nGaRGDeyhdyTdoSvh6nVgrmEYazk6BHafYRh3AtHAJuAB4BNgmWEYAL8B/gI8YxjGao6OZN5/jkOC\nIiIiQcXn97F4zyusr9hEelQqIxOHEx0aRUxINLFhMQyLH0qoI6TLrxfiCCEjOo2M6LQerFp6wznD\n1bHRqIdPefjEccszTfl654UWJSIiEshODFbZMZl8c9xDRIZEWF2WBAhNIioiInIefH4ff9z4POsr\nNpETk8Wj4x5UsJKTKFyJiEi/9fbB99lTt49rh1yFkZjbpXVe3vsmq8rWKVjJGekujiIi0i+tKf+U\n9w5/zKGmYn677Sn+unMxDZ2NZ11nb/0BVpWtIzsuQ8FKzkjhSkRE+p0DDYd50XyDKGckD425h0Gx\n2Wyu2s4P1/+cj4pX4vP7vrCOx+fhRfN1bNh4eNKXFazkjBSuRESkX6nvaODpgmfx4+f+0XcxLnk0\n/zLhEe4ccRNOu5PX97/D4sJXvhCwlpV8QmVbFTMyppCbNMia4iUoKFyJiEi/4fK6+FPBIprdLdyY\new0jEocBYLfZmZ5+Kf855V/JiclifeUmXtm3hM/uYlLbXs97hz4iJiSa64ZcaeVbkCCgcCUiIv2C\n3+/n+cKXKWkuY2raJGZlTv/Cc6JDonhk3P2kR6WysnQNSw6+D8Ar+97C5XNzQ+4CIkMie7t0CTIK\nVyIi0i+8dXApm6u2MyQuh9uMG7DZTn9ruOiQKB4d9xApEQN4v2gZTxc8x46aXeTGD2Zy6iW9XLUE\nI4UrERHp81aVruODouUkRyTx1TH3EmI/+0xEcWExfHP8QySExbOtugC7zc5tw88cyEROpHAlIiJ9\n2o7qXby09w2iQ6L4Rv6DxIRGd2m9xPAEvjX+ITKj01k49GrSo1N7uFLpKzSJqIiI9FmHGov5666/\nE2J38kj+/SRHJp3X+imRyXxn8uM9VJ30VQpXIiLSZ3R4OihrqaSspZzSlnK2Ve3E4/PwtbH3khOb\nZXV50k8oXImISJ+wtnwjf9/zCn78xx8LsTu5c8TNjBmQZ2Fl0t8oXImISNBz+zy8dfA9whyhTEuf\nTGZ0Opkx6QyMTMZ5jpPXRbqb9jgREQl6m49so9nVwhXZM7khd4HV5Ug/p6sFRUQkqPn9fpaVfILd\nZj/txKAivU3hSkREgtre+gOUtVQwPnkMCeHxVpcjonAlIiLBbVnJKgDmZH/J4kpEjlK4EhGRoFXZ\nWsXO2j0MicthUGy21eWIAApXIiISxJaXrgZgTtZlFlci8jmFKxERCUot7lY+rdhMUngC+cmjrC5H\n5DiFKxERCUqryz7F7XMzK2sGdpt+nUng0N4oIiJBx+f38UnZOsIdYUxNm2R1OSInUbgSEZGgc7ip\nmIbORsYljyHCGW51OSInUbgSEZGgs+XIDgDGp4yxuBKRL1K4EhGRoOLz+9haXUCEM4IRicOsLkfk\nCxSuREQkqHx2SDB/wCjdlFkCksKViIgEFR0SlECncCUiIkFDhwQlGChciYhI0NAhQQkGClciIhI0\ntlTpkKAEPoUrEREJCj6/j61VOiQogU/hSkREgoIOCUqwULgSEZGgoEOCEiwUrkREJODpkKAEE4Ur\nEREJeIcadUhQgofClYiIBLxNR7YBMGFgvsWViJybwpWIiAQ0r8/LlqrtRIdEYSTkWl2OyDkpXImI\nSEAz6/fT4m7lkpSxOOwOq8sROSeFKxERCWifHxIcZ3ElIl2jcCUiIgHL7XWzvXoXCWHxDInLsboc\nkS5RuBIRkYC1q3YPHd4OJg4ch92mX1kSHLSniohIwNIhQQlGClciIhKQ2j0d7KwtZGBkCpnRaVaX\nI9JlClciIhKQdlTvwu3zMHFgPjabzepyRLpM4UpERALSpqqjhwQn6pCgBBmFKxERCTgtrlb21O0j\nOyaDlMhkq8sROS8KVyIiEnC2VG3H5/fpRHYJSgpXIiIScNZWbMRus+uQoAQlhSsREQkoxc2llDSX\nMTppJPFhcVaXI3LenOd6gmEYduBJIB/oBB40TXP/CcvvAB4HPEAB8MixRWdcR0RE5EzWlm8EYFr6\nJIsrEbkwXRm5uh4IN01zKvDvwC8/W2AYRgTwY2C2aZrTgTjgmrOtIyIiciadXhcbK7cSHxZHXqJh\ndTkiF6Qr4WoGsBTANM31wMQTlnUC00zTbDv2sxPoOMc6IiIip7Wlagcd3g6mpk3EYXdYXY7IBTnn\nYUEgFmg84WevYRhO0zQ9pmn6gCMAhmF8E4gGPgRuPdM6Z9tQcnLMeRUvXaO+9gz1tfuppz0jmPq6\nccdmbNhYMHo2yVGBXXcw9TWY9IW+diVcNQEnvlP7iSHp2DlZPwOGAzeZpuk3DOOs65xJdXVz16qW\nLktOjlFfe4D62v3U054RTH2taD2CWXOAkYnDsbWFUt0WuHUHU1+DSTD19WwhsCuHBdcA8wEMw5jC\n0ZPWT/QnIBy4/oTDg+daR0RE5CRryzcAMC19ssWViFycroxcvQ7MNQxjLWAD7jMM406OHgLcBDwA\nfAIsMwwD4DenW6cHahcRkT7C7fPwaeVmokOiGDsgz+pyRC7KOcPVsfOqHj7l4T0n/PlMo1+nriMi\nInJaO6p30upu4/Lsy3Dau/K9XyRwaRJRERGx3OqyTwGYlqZDghL8FK5ERMRSZS0V7G04wPCEXFKj\nUqwuR+SiKVyJiIilVpSsBmBO1gyLKxHpHgpXIiJimWZXCxuObGVARBKjkkZYXY5It1C4EhERy6wu\n+xSPz8PszBnYbfqVJH2D9mQREbGEx+dhVdlawh3hTEmbYHU5It1G4UpERCyxpWoHTa5mpqVPItwZ\nbnU5It2Xr19YAAAgAElEQVRG4UpERHqd3+9neclqbNiYmTnd6nJEupXClYiI9LqDjUUUN5cyNnkU\nAyISrS5HpFspXImISK9bXnp0+oXZmZp+QfoehSsREelV5S2VbKsqIDM6ndz4wVaXI9LtFK5ERKTX\n+P1+Xtn3Fn78XDvkSmw2m9UliXQ7hSsREek1O2p2YdbvJy/JYPSAkVaXI9IjFK5ERKRXuL1uXtv3\nNnabnZtyr7W6HJEeo3AlIiK9YlnJJ9R01DErc7pu0Cx9msKViIj0uIbORpYWLSM6JIqrB11hdTki\nPUrhSkREetxbB5bi8rq4bshVRIZEWF2OSI9SuBIRkR51sLGITys3kxmdztT0SVaXI9LjFK5ERKTH\nuH0eFhe+DMAtwxdit+nXjvR92stFRKTHLD30EZVtVVyWMU0Thkq/oXAlIhfN7/dbXYIEoJLmMj4o\nXkFieAILh15ldTkivcZpdQEiEpxcbi+f7j7Cx1tKqahtIzM5mkFpMQwaGMPg9FgyBkRp9u1+zOvz\n8nzhy/j8Pu40biLcGW51SSK9RuFKRLrM7fFSVd/Oul1HWLW9nJZ2N3abjdSkSIqPNHOooun4c0dk\nx3PL7FwGp8VaWLFY5cPilZS2lDMlbSIjk4ZbXY5Ir1K4EpEzqqxr4931RVTWtlHd2E5ji+v4suiI\nEBZMzWH2+AwSY8Nxe3yUVrdwuLKZrXur2Xmojh8t2sTEESncdNkQBiZGWvhOpDeVt1Ty3qEPiQ2N\n4abca6wuR6TXKVyJyBd4vD7eXV/E22uL8Hh92G02EmPDGJmTQFJcOEZWPJNHphDidBxfJ8RpZ3Ba\nLIPTYpk9PoPConpeWbGfTXuq2Lq3mhlj07hm6iCS4nR4qK9qc7fxQdEKVpSuxuP3crtxA5EhCtXS\n/yhcichJ9pY0sGjpHipq24iLDuWuK4YzfvgAHPbzu/5lZE4C/3HPRDab1by66iArt5WzekcFl41L\nZ8GUHBJjFbL6CrfXzcqytbx/eBltnnbiw+JYOPRq8pNHW12aiCUUrkQEgANljSzdUMxmsxobMPuS\nDG66bCiR4Rf+MWGz2Zg4IoXxwwewftcRlqw5zPItZXyyvZx5k7K5ceYQ7DrpPWi1udtYXf4pK0rW\n0OhqIsIZwfVD5zMzczqhjhCryxOxjMKVSD/m8/vZvq+GpRuK2VfaCMDgtBjuvGI4QzPium07Drud\n6WPSmDJqIGt3VrJkzWHeXV9EfXMn9y8Ycd6jYmKtmvY6lpd8wtqKjbi8LsIcoVyRPZN5ObOJ0mFA\nEYUrkf7I7/dTcLCWl5cfoKymFYCxQ5O4anI2RnZ8j02h4LDb+dLYdCYMT+b/XtrOul2VeH0+Hrwm\nr0e2J93vYGMRv9n6Jzw+D/FhccwfdAXT0y/V/QJFTqBwJdLPFFU289Ly/RQW1WOzwbTRqVx9aTYZ\nydG9VkNkeAj/cts4fvPydjYUVuH2+Pjeg1N6bftyYTo8nSza9Q+8Pi93jbiZS1Mn4LA7zr2iSD+j\ncCXSTzS2dPLyigOs21mJHxg9JJFbZ+WSmdJ7oepEEWFO/unWcfz21R1s3VfDD55ez/DMOMJCHISH\nOogMc5I3KJGwUP3yDhSv7V9CTUcdc7NnMS19stXliAQshSuRPs7r87F8Sxmvf3KQ9k4vWSnR3Do7\nl1GDE60ujbBQB4/dPJYnXi9gx/4aduyvOWn5wIQIvn79aLIHxlhUoXymoGY3a8o3kBGdxoIh86wu\nRySgKVyJ9GEHyht57n2T4iMtRIY5uXvecGaOy8BuD5wr9EJDHDx+cz71HR7KKhrpcHnpcHkpqmxm\n+dYyfvzsJm6bM4w5l2TodjoWaXa1sLjwFZw2B1/Ju4MQu351iJyN/oWI9EF+v58law/z5ieH8APT\nR6dyy+xcYqNCrS7ttOx2GyNyEkmKPOHy/XzIz03iz28XsvjDvRQW1bNwxmDcHh+dLg8dLi9RESEM\ny4xT6OpBfr+fxXteodndwo2515AenWp1SSIBT+FKpI/xeH08u9RkdUEFSbHhPHRtHsOz4q0u64KM\nHTqA/7p/Mn96axdb9lazZW/1F54zYXgyX77SIC5Ag2OwW1u+gYKa3QyPH8rsrBlWlyMSFBSuRPqQ\n9k4PT75ewK7D9eSkxvD4zWOJiw6zuqyLkhATxr/dMZ6Pt5RSUdtGeIiDsFAHYSEOtu2vYfPeavYU\n13PX3OFcmjdQo1jdqLylkpf3vUWEM4K7827FbtN8ZCJdoXAl0kfUNXXw65d3UFrdQv7QJB5eOLrP\nXGlnt9uYOzHrC4/Pm5zF8i1lvLxiP08t2c2GwiruXzCS6AjNDn6xXF4Xf9m1GLfPzX2j7iAxPMHq\nkkSChr6GiPQBhUX1/HDRJkqrW5g9PoNHbxrTZ4LV2dhtNi6fkMkPH7iUEdnxbNtfwxOvFeDx+qwu\nLei9vPdNKluPMDNzuu4RKHKeFK5Egpjv2Inrv3hhK63tbu64fBhfnje8391OJiU+gm/fMZ6JRvLx\nG0/7/X6rywpamyq3srZiI1nR6dyQu8DqckSCjg4LigSplnY3Ty/ZTcHBWhJiwvj69aPJ7cb7AQYb\nu83GA9fkUdO4hTUFlaQlRTF/Ss5JzzlQ1ojb42NETt84xOVta8Pb2IC3pRVvawve1hZsNjv2iAjs\nkZE4IiPp8CXj7fBhD4/A1oXQXdVWzd/NVwlzhHL/6Ls07YLIBdC/GpEg1Nbh4ceLNlHV0M7owYk8\ndG0eMZG6Wi4sxMG3bh7LjxZt4pUVBxiYEMElw5PZcaCWd9cXsa+0ERvwrZvHkp87wOpyL4jP7aJl\n6xaa1qymbfcuOMcIXdFnf7DZsIeHYw8Px+/14vd48Lvd+H0+4i6bScqdd1PWUsHTBc/S6XXxlbw7\nSIlM7vH3I9IXKVyJBKEXl+2jqqGdKyZkcvsVw7DrCrnj4qPDeOzmsfzv81t4eslukhMiKKs+enPq\n0YMTMUsaeGrJLv7jnomkJUVZXG3XuaqrqH9/Kc0b1uNrawMgfMgQQjMycURF44g++h9+P772drxt\nbfja2gjxuWlraMLX1oa3rQ1/Rwe20DBsTie2kBC8TY00Ll9GpbuBvw2qxO3zMH/QFUxKHW/xOxYJ\nXgpXIkGm4GAtn+yoIDslmlvn5CpYnUb2wBi+el0eT7xaQHlNK1PyBnL1lByyUqJZt6uSp5fs5nev\nFvAf90wkMjywPwZ9bhf1S9+j7p0l+D0eHHHxJFw1i7jpMwhNSz/n+snJMVRXN59xeUdjHeaPv0fE\n6i2M74jnkpu/ypgBed35FkT6ncD+VBGRk7R1eHjmvT047DbuXzASp6N/nbh+PsYPS+Y/vzKJqHAn\nA+Ijjj8+dVQqxUeaeX9DCU8v2cU3bx4bsAG1dddOqhY/h7vqCI64eJJvvY2YiZOxObrnStCqtmr+\nai6m4bJw7vjQxdRNDaSOa4DgPGIqEjAUrkSCyAvL9lHf3MnCGYN1M+MuyEk9fY9unjWU0qoWth+o\n5Y1PDnHjZUN6ubIzcx05QmvBdlq2baV9TyHYbMRfMY+khTfgiIg49wt00acVm3lx7+t0el1My53C\n0PxJVP7iZ1T+7S84omOIGj2m27Yl0t8oXIkEiR0Hall97HDggqk5515Bzshht/O1haP50aKNvL32\nMINSY7hkuHUnb3tbWqhb+i4tWzfjPnLk+OMRww2Sb7+T8Ozu+/vu8HTwgvkGG49sIdwRxn15dzDx\n2PlV6Y8+RtmvfkH5k78j41v/ROSIkd22XZH+ROFKJAg0tHSyaKkOB3an6IgQvnnjWH787Cb+8k4h\nWSnRJMd338hQV/h9PhpXraDm9VfxtbZiCwsjavwlRI0ZS9SYfEISunfKiJLmcv6y8zmq22vJicni\nvlF3khyZdHx55HCDtK9/g4onn6Dst7/qcsDy+3xdmuZBpL9QuBIJcJvNKhYtNWlpd3P9l3Q4sDtl\npkRz17zh/O3dPfzhjZ1858sTCHH2Tkho37+Pqr8/T2dxEfbwcJJvvZ242ZdjD+mZW/dsqtzK83te\nwe1zMzd7FtcMmYfzNHNYRY8dR9rXH6XiD6cPWB3FRbRs3oTryBE89XVH/2toIGK4Qcajj2EPD++R\n+kWCiS1QZjFu6mzx19a0HP85whmOw973b9/R0851pZBcmN7oa3unh79/uJc1OysJcdq5dXYucy7J\n6LM3JrZyX/3L27tZs7OSyydkctfc4T2+vcZPVnJk0d8AiJk6jeSbbsUZH98j20pMiuTP61/i45JV\nhDvCuCfvdvKTR51zvZYd26h48gmw20l94CHcNTU0rVuLq7Tk8yfZ7TjjE7CFhuCurCRy1Ggyvvk4\nNmff/96uz9aeEUx9TU6OOeOHccD8C3jwjX896eeEsHgeHfcAqVEDLapIgkl9RwPrKjYSGxrDuJQx\nRIf0zvxFfr+fhs5GYkNjuvXLwKGKJp58fSe1TR3kpMbw1WvzgmpOpmDz5XkGhyqb+XhzKUZWPBNH\npPTYtjoOH6Jq8XPYo6LIePQxIob1XJhrcbfyx1V/oeCIycDIZL465l5So7r23qLHjiPtkUepePIJ\nKv7w+6MPOhxEj59AzNRphA8egjMuDpvdjt/rpfz3v6V1x3aOLPobA+9/sM9+CRDpinOOXBmGYQee\nBPKBTuBB0zT3n/KcSOBD4AHTNPcce2wL0HTsKYdM07zvbNv5xZo/+V2dXgA8Pjc7a/cQExrNY+O/\nRtppAlZNex0xodGEObp3Vmq/30+Tq5nSlnJKm8tpdDVx9aAriAmN7tbt9JZg+hZwIWra6/igaDnr\nKzbh9R/df+w2OyMShzExZRzJkQOoaKmkrLWC8pZKml0t5CYMYXTSCIyEXEIvYP9pcbdS4Sljw+Ht\nFNbto76zgdSogTwy9j6SIhIv+j0dKG/kly9so9PtZcHUQVw3fVC/OMfK6n21vKaVHy7aiMNu49u3\nj2dwWmy3b8Pb0kLRj76Pp66OjMf+uUevyCtpLufpgkXUdtQzZsBI7s27nQjn+Z9T1rqzgIaPPyRq\nzFhiJl2KI+b0h6V9nZ2U/uKndBw6SMLVC0i+6ZaLfQsBzer9ta8Kpr6ebeSqK+HqRuA60zS/YhjG\nFOA7pmkuPGH5ROCPQCYwyzTNPYZhhAPrTNM8nyl+/Sc2dGXpWl7a+wYxIdF8a/xXSY9OBaCytYrX\n9r/Nrto9RDkjmZk5jZlZ0y96pKLT6+Ltg++zsXIrze6Wk5blDxjFQ2PuCcpvYsG0o3aFy+vmSFsV\nFa1H2FO3j41HtuLz+0iJGMAVOTNp93Sw+cg2ipvLvrCuDRshjhBcXhcAIXYnufFDcNqdtLrbaHO3\n0eppI8weSkpkMimRA0iOHEC4I4zK1ioqWispbz1CXUf98deMckaSGjWQA42HiAmN5utj7yMnNuuC\n39+hiiZ+8cI2Ol1evnpdHpNH9p+R20DYV9ftrOTpt3cDkJsRx8xx6UwakUJoyMWPSvp9Psp+83+0\n7dpJ0sIbSLp24blXukAnnl9186gFzEz5EnZbzwd0b3MzxT/5b9xHKkm+/S4Srpjb49u0SiDsr31R\nMPX1YsPV/wEbTNN84djPZaZpZpywfDpQDDwHPHwsXF0KPMvR21o5ge+aprn+HHX6T23oqtJ1vLj3\ndaJDonhw9N1srS7gk7J1+Pw+BsVmU91WQ6unjVB7CNMzLuWK7JnEh53/jWv31R/g+cKXqemoIzY0\nhsGx2WTEpJMZnc6yklXsbzjEV/LuCMrbQQTTjnombe52PihaztbqAmrb6/Dz+T6bGjWQq3LmMGFg\n/km/PKraqtlStYNWdxtpUalkRKeSFjUQh83BoaZidtYUsqt2D+WtlcDR4BUVEkmkM4J2bwfNrpYv\n1AEQGxpDelQq4zJHkh2WQ1ZMBnabnRUla3hl31s47U7uG3UH+cmjT7u+1+elrLWCytYqhsUPISH8\n8/Nsiiqb+fk/ttLu8vDQtXlMyUvtjvYFjUDZV3ccqOGjzaXsOliHH4gKdzJ3YhbXTBuE3X7hX7Bq\n3nyduiVvEjl6LBnferxHrq7z+ry8eeC94+dX3Zt3O5fnTenVvrqrqyn+yY/xNjYy4JbbSJh3VVB+\nMT2XQNlf+5pg6uvFhqs/A6+apvnesZ+LgSGmaXpOed4KPg9XY4ApwJ+BYcB7gHHqOqf4QrgC+KRs\nPS+Yr33+ZiKSuCH3GsYOyMPlc7O2fAMfFa+kobORqJBIvjnuIbJiMr7wOjtrCnn9wLtEh0QyODaH\nwXE5ZEan8XHJKlaWrsWGjSuyZ7Jg8FxCHJ9frVPTXst/b/gVTpuD/7j0X4gL6/5DBT0pmHbUU3l9\nXlaXf8q7hz6kxd1KhDPiWEhKJTUqhYyoNIbGD7qob+QtrlbsNjvhzrCTXqfd005VWw3VbTW0eztJ\njUwmLSqV6NCjI6Sn62tBzW7+unMxbp+HGRlTiA6JPB4DOzwdFDeXUtJchtt39J9BqCOUhUOu5rLM\nqZQcaeUXL2ylrcPDg9fkMXV0/wpWEHj7anVDO6u2l/PJ9nKa2tyMHZrEV68ddUG3y2kt2EHZb3+F\nMzGRnO/919F7AJ6Dx+eh1d1ObGh0l8JJTXstzxe+zL6GgyedX2VFXzvLSin79S/x1NcTf/lckm+7\no89N1RBo+2tfEUx97Y6Rq/Wmab507OdS0zQzT/O8FXwersIAu2ma7ceWbQBuMk2z5NT1TnDGQpYd\nXMube95n7tDLuCp3Jk7HyR9uHq+HpftX8ty2V4kMCee7M7/JsKTBx5cvP7iWP21ajA3w4efU95wR\nm8ojk+85aZ0TLd23gr9ueZGJ6WP51xkPn/RBZ9YcwIaN4QMCZ4bnvmBHZSF/2/ISZc2VRDjDuSHv\nKuYPm02os3vPsetuB+uK+eknT1Lf0fiFZXabney4dHITB5EYmcA7ez+m1dVGRlQW5VuH0t4YzmO3\njefySdkWVC5n0tLm4ufPb2aLWUVmSjTfu/9S0pO7fg5ms7mXnf/5X/g9Hsb85L+JGZZ7znX21x7m\n52v+SH17I7Fh0eTEZzIoPpPBCdmMTR1JbNjn2/f6vLyz92Ne2vk2Lq+bSRn5fGPyvUSG9u6cXafq\nrK5h94/+m7aiYhKnXMrwf34MR1iYpTWJdLOLClc3AdeecM7V903TvPo0z1vB5+Hq68AY0zQfMQwj\nHVgGjL6QkavzsaFyC88VvkSoPYSv59/P0LhBLD28jLcPvU+UM5KH8+8jLWogRU0lHGosoqi5lKzo\ndOblzD5ptOpUPr+P3259in0NB7k373Ymp17CwcbDLDn4AXvr92PDxpdH3sKUtIkXVX9PCKZvAZ85\n0HCYX2/9I36/n+npk1kwZB6xoYE1t9PZ+truaae0uZzP/t3ZbDacdgfpUaknnUDf5GrmT5te4nCH\nid9nY2z0ZO6fdB2hZ9kX+7JA3le9Ph+vrDjA+xtKiAxz8vD1oxg9OOmc63WWlVLy0//F195G2tcf\nJeaSCedcZ33FJv5hvobX52VE4jCq22qo6ag7vtyGjcFxOYxJGkl6dCpvH3yfkpZyokOiuGXYdUwY\nOO6kL4BW9tXb1kr5k0/QvqeQ8KG5ZDz62BlPiA82gby/BrNg6uvFjlx9drXgWI7+trgPuASINk3z\nqROet4LPw1Uo8AyQzdERqf/PNM2156jzosMVwNaqAv66azFOm4O8JINt1TtJDE/gG/kPdPkS5NOp\naa/jvzf8Hw6bg0GxWRTW7QVgRMIwiptLafO0c9vw67ksc9pFv4fuFEw7Khw9TPe/G39NY2cTj457\nkBGJw6wu6bS6o68fbCjmxWX7CRtQQ/Qwk1ZvMwPCE7ll+EJGD+h/tx0Jhn11TUEFi5buweP1M8FI\nZuH0wWSmnH4Uy1VVRclP/xtvYyMD73uQuOkzzvraXp+X1w+8w/KS1UQ4I7h/1J3kJRnA0cBe1lLJ\nwYbD7Kwt5GBj0UnnHk5JncgNwxac9sIeq/vqc7s58sxfaP50PSEDU8l4/J8JTe65qS56i9V97auC\nqa8XFa56UbeEKzh67sufC57D4/eSEZ3GI/n3X9CJ7qdaVbqWF/e+AcDwhFyuGTyPofGDKGup4Hdb\nn6bZ3cL1Q+czN2fWRW+ruwTTjurz+/jDjr+xu9bk2iFXcdWgOVaXdEYX09dOl5dXVx7go82lxEWH\n8k+35JOSFMK7hz5ieelqfH4f+cmjuXnYtSSGd+/tTwJZsOyrB8ubWPzhXg5VHJ1pZuKIFK6bNojY\n6FBa2920tntora4hYvHvoaHunFfNNXY2sbvWZG3FRg42HiY1MoWvjb2XlMgz3+uwxdXK7jqTw03F\njB0w6qxfQgKhr36fj5rXX6X+vXdwxMSS8a3HCR8c3KdSBEJf+6Jg6mu/C1cA++oPsrO2kKsGzbmg\nuV1Ox+f3sa5iI8kRAxieMPSkZUfaqvnd1qep72zgypw5XDNkXq9c+nwuwbSjflC0nDcPvMfIxOE8\nkn9/QPTvTC6kry3tbj7eXMrHm0tpaXeTPiCKf7oln6S4z28XUtZSwYvmGxxoPESIPYQvZUxhbs6s\ngDss2hOCaV/1+/0UHKzljU8Ocbjy85qdPg+jmw8wtX4ncZ5WViWOozBnEhkDojCy47lycjZOh51m\nVwvLS1azq3YPpS3lx9fPHzCKu/NuI8LZfbeQCaS+Niz/mKq/P48tJIS0hx8heuw4q0u6YIHU174k\nmPraL8OVFWrb6/nttqeoaa9lSNwg7jBuPD4/l1UCbUdt6GxkV+0eqttqyYxOY1BcNknhiRxoPMxv\ntv6JmJBovjP58YCftPV8+trU5uKdtUWs3F6Gy+0jKtzJ5RMymTcp+7RXnvn9fjZUbmHJwfep72wg\nxB7CZRlTmZszK+D7cjECbV/tCr/fz/b9tWzYYJJTvJ3Mkh2EuDrw2x1U5k1lc+oEymrbqG/uBGBo\neiwLrojl5UMv09DZiNPmIDd+CHlJBnlJBqmRKd0+bUGg9bVl6xYqnv4jfreb1PseJHbadKtLuiCB\n1te+Ipj6qnDVi5pczbxkvsHW6gLsNjtXZM/k6kFXnPEk5RZXK9uqC45evp9+6VlPrL8QF7Oj+v3+\n4+d2ZEanEd6Fb9ONnc2sLd+Ay+cizBFGuCOMMEcoVe017KrdQ1lLxRfWiQ6Jwuv30eHp4PFLHiY3\n/vRXbQaSrvZ156Fa/vJ2IY2tLhJiwrhycjaX5acRHnruy/ndPg/ryjfyftEyGjobCbWHMCtrBnOz\nZxEZYu2VYD0hmD5UP+Pr7KT27beo/2ApeL04omOInjmT2vGDCU9IIicmC4fdQUu7m8UfmWyq2UBo\ntonNBtcMuZLZWTO6/S4TpwrEvrYf2E/Zb3+Fr6ODrH/7DhFDz30FZaAJxL72BcHUV4UrCxTU7OZF\n8w3qOxsYEJ7I6AEjSQxPIDE8gYTwOMpaKthyZAd7Gw7g8/sAyIhO4/5Rd13UifenupAd1e/3s7O2\nkHcPfURxc+nxx1MiBpAVk0FObBZGQi7p0anHD901u1r4qHglK0vX4va5T/u6TruTYfFDGJU0grSo\ngZS1VFDUVMLhpmJqO+q5IXcBV2TPvPA324vO1Ve3x8srKw7y4aYSHHYbN142hLmTsi7oNjZHQ9YG\nlh5eRqOriUhnBPNyZjMzc9oF3b4nUAXThypAy/ZtVP3jeTw1NdgTEmj40lg2Z3gobD50fC6zCGcE\nIxJyyUsyKKzby5aqHfjdobj35zN/zASumzEIRw/P/xSofW0r3E3p//0cZ3w82d/7L5yxmkNQgquv\nClcW6fS6eOfQBywvWX08QJ0qJzaLCSn5VLVVs7r8U0LtIdxm3NBt0zqcuKPWtNdR11FPdkzGaUeh\n2txt7Knfz/uHl1HaUo4NG+OSR5MYkUBJczklzWW0e9qPPz8mJBojMZeY0GjWlm+g0+siPiyOK3Pm\nkBGdRqe3kw5vJ52eTmJCoxmWMPSM39JdXndQTUFwtg+Aospm/vJOIaXVLaQmRvK160aRk3rx50y5\nvG5Wlq7hg6LltHnaiQuNZcGQuUxNm9Rt56c1dDZS0lzGqKQRvX7OW7B8qHoa6jmy+Dlat27Bb7dz\nMD+VpcPceJxHP2fTogYyOmkk7d4OdteaJ90uaUjcIOalXMdzbxdT09hBzsAY7r3aYFBqzwWLQO5r\n3btvU/PaK0SMGEnmP30bm6P7bn7e0wK5r8EsmPqqcGWxNnc7NR211LXXU9dRT11nA3GhsVySMvak\nG/1uqdrB4sJX6PB2MGngeCYOHEdyRBKJEYmE2M98GMnn91HWUkmTq5kRCbk47J9/QH22oxbW7eWP\nO57B4/Ngw0ZqVAo5sVnEh8VR0VJJaUs5tcd+CdiwMWFgPlfmzDnpnDG/309tRx0HGg5j1u9nT91e\nGl1H/85iQ2O4MmcO09Mnd/uhzUB06geAx+tjy95qlm0uZW/p0QlEZ41L57Y5wwgL7d5fGG3udj4s\nXsHyktW4fW7So1K5adi1FzVthc/v45P/n737jm+ruv8//rraw5a85G3HGbbi7OHsTRJWGWFTdiib\nQmnLr/1200J3aaFQKKPsvWdDBiGb7L3kOE68h7xky9rS/f3hEEjJcBIr0rXP8/Hwo9SW5I/eub7+\n+Nxzz6lZy0f7F+AL+xlpG8YNxVdh0Jy5RR+VcFL1lu+n6rF/QIeb2nQdn5ck0JLUNSI7Kn04w1KL\nSfvGz7QsyzR6nOxuKUUlqZiaPQG1Sk2nL8gbS/axemc9kgRzS/KYN61/ty4Xn6x4zlWORKh94jE6\nt24h+dzzsV1+ZaxL6rZ4zlXJlJSraK4UpMnbwnO7XqWi/evF7CUkkvRWbKY0bMYU0oyppBlT8Yf8\n7G3dx96WfbiDnUDXuls3D7sWs9YEdB2oq0u38sS255CRmZw1nrrOeio6qg9vYAxdo1C5idnkJeYw\nMXMsGd24NCnLMvWeRho9TRSnFPaqS1Qn8tUJwOX2s3xrLcu21tDm7spzWP8Uzhmfz9D+KSd4ldPT\n5iq0c8UAACAASURBVHfx8f6FrKvfhIzMsNRizu8/h2xz5kk1uDXuOl7b+y4H2ysxaoykG9Oo6Kgi\nJyGL24ffRKrxzCwHEauTaqitlXCHm4jPS9jrRQ4EMPQrQGv7eikEb8jLtsVvY/ngC1QRmVWjE6ga\nmcOE7BLGZ449oqE6GbsPtvDSQgeNrV5SLXpuv2gYg3JPf9mYb4r3X1Zhj4fKh35LsLGha6HVsfG3\nGPPRxHuuSqWkXEVzpTChSIidTXuo9zhxepto8jbT5G2hzf/tLVUArDoLg1MKaQ90jVClGVK4bcSN\n5CRk0UwjDy3/J+FImNuG33B4ccpwJEy9p5F2fwfZCZlYdIm9cnPVaJBlmRZPiHc/L2XD3kbCERmj\nXs2U4VmcNSaXzBTTGa2nsqOa9/Z9wr62cuB/m/FUsswZhz8sukT84QA17jqq3DVUtFexsWErETnC\n2PSRXFZ4EQlaE2/t+5BVNWtJ0Jq5bfiNDEwqiPr7ONMnVVmWaXrnTVoXfnbUrxsGFWIaP55VqS48\ny5ZTsqODgEZi33dGMWzqBQxKGtAjPzOBYJiP1xzks3WVGPUaHpg/jhRL71yK4Vj81VVU/uFBALLv\nvhfz0KNvfB5PlJCrEikpV9Fc9RKBcPBQo9WM09uMWlJjTxl0+PbtiBzh0wOL+ezg5+hUWs4pmM2S\nymX4wwFuGXY9I21DY/0WFO2rtY3eX3mAikNrG+WkmTlrbC6ThmZE5ZLOydS2s3kP2527cXqbcHqb\nj9qMG9QG/GH/Eat7pxqSucp+CUNTBx/x2BXVa3h730dISJxXMIfZ+dOjOi/uTJ9Umz/+kOYP30eb\nnoF52HBURiMqgxFUEp07tuN17AW5KykJCCYlkPn9e0ktKIpKPUs3V/PKolIG5lj46TVjTunmh6NR\nyi8r99Yt1P37X8iyTObNt2KZMDHWJR2XUnJVGiXlKpqrPmZr4w5e3PMmgXAASZK4eei1jEkfEeuy\nFK2q0c2bS/ex+2ArkgQTh2UxdVgmg/OT4nbELxAO4vQ2UdfZ0PXhrqfB4yRRl0BeYg65CV2XgTNM\ntiPm6X3T3pZ9vLDrdTqCbpL1SVw88DxK/mfvup5yJk+qrUsW43zjVTRpaeT99Bdok7++9BmOhFlw\ncAmr9iyh8KCXkloNKWm5ZN9ye1T3xZNlmac/3s263Q3MLcnju3N6ZusnJf2y8jj2Uvv4o0S83hOu\nbB9rSspVSZSUq2iu+qBadz0f7P8v59inMdAQn/vzxTtZlqlr9rBwfSWrttchA8MGpHDVrEGMGpKl\nmBPA6fKGfCyq+IKlVSsJRUIUWPKZkj0eq96CRZeIRZdIoi7htO8uPFMnVdfqVTQ8/yxqq5W8n/4C\nXfrX8wsPuCp4s/QDqjpqSDEkc33xld/ajSGafIEQD764kbpmD3fNG0bJ4NNflkVJv6wA/FWVVD/y\nMGGXi5TzLyD1ksvi8g8YpeWqFErKVTRXfZiSDtR40OkLUlrZxo4DLewsb6bJ5QMgx2bmqlmDGDYg\nFeibuTZ7W/hw/wI2NW771tdMGiMzcqcwM2/KUTcP7o4zkal7yyZqn3gcldFE3k9/hj4nF4BWXxsf\n7l/AhoYtQNdGyJcXXdSj29B0V21TJw++uBFJgl/fNO605/Ap8VgNOp1U/+NvBBsbSL/2BpJmxd8+\no0rMVQmUlKtorvowJR2oZ1q7J8CXO+updrppaPXS0OKhw/P1AqgmvYah/VMYXZjGuOL0IxZ77Mu5\nVnXUUt1RQ3ugg/ZAB65AB6WtZXQGPejUOqZmT2B2/vST3iw92pn6qyqp/ONDIEnk/vgnGAcMJBAO\nsrhyGYsrlhGMBMlPzOGywotivkvA2l31PP3xbrJSTdx/9WiSE099SQylHqvB1lYqfvML5HCEgt89\nhDY1LdYlHUGpucY7JeUqmqs+TEkH6pnS5PKycH0VK7fVEgh1Le6qkiTSkgxkJJvol5nIiAGp9M9O\nPObq2SLXI/nDAVbXruPzyhW0+V2H71hMMSSRbEjq2plAn3T4/yfrkzBo9ATCQQKRAIFwEFtqIpFO\nTVQuAYXd7q7b/ZucZN99Dwmjx1LuquDlPW/S6GnCokvkogHnMiFrbNxsGP7WF2V8tq6SNKuB+787\nmvSkU9vySMnH6leXcE1DhpLzw/vj6vKgknONZ0rK9XjNVexubxKEKNtR3syeg62oVBJqlYRaLVHf\n4mH97kYiskyqRc/Z4/MZ1j8FW5Kxx+7O6ov0ah1n5U1jWs4kNtRvZm3dJpp9LV17U7oOdvt10o1p\njLANZUTaUPpb80+50WnxtbK+fgtbG7djVpuYtagKXZOTlAsuQjdiBO+VfcLSypUAzMqdygUDzu7W\n3pln0hUzB6LTqPho9UH++MomfnzVKHJtvXfj7qOxTJ5Cx4b1eHZup331SqxTp8e6JEHoFjFy1csp\n6a+AntTY5uXXz647PDL1TTlpZs6bmM/44oxTbqj6aq4nKxwJ4wq00+Jro/XQR4u/jVZfK/5wAL1a\nh1atQ6fSElaH2F6/5/DitonaBAqseeSYs8hOyCInIRONSkub34XL76LN34435EOv1h360BOMBNnU\nuJ19rfuRkVFLaiZucVGy28OBHD3ll4zH6WuhwePEZkzluuIrY34J8EQWb6ji9c/3YTZouO/KkQzM\njq/LrdEWbGmm4te/AEmi3+/+cMSdnbGk9FzjlZJyFSNXQp8iyzIvL3QQCEX47uxC+mdZCEcihCIy\neq2aAdkWVHF0eaE3U6vUhzcsPxGbLZHa+hYcrWVsc+5iV/NedjTtYUfTnpP+vgOtBUzIGsvgyiDN\nu58lmGJhx5x8DrQ4kJCYlTuViwaeq4hdBeaOy8Oo1/D8gj387fWt3DlvKCMGxtf8o2jSpqSSdsXV\nNL78Ao0vv0D2PffF1eVBQTga0VwJvc663Q3sOtDCsAEpzCnJFSdiBdGqtQxLKz68k0BHwE2Nu45a\ndx01nfWEIxGSDVasegtJeismjZFAOIA/HMAf9hOWwwxOLsJmSsW7r5Tql/6GpDcw6L6fcn92Dg0e\nJxKQbrIdv5A4M3VEFiaDhqc+2sWj72zn2rlFnDUmN9ZlnTHW6TPo2LCOzu3baF+1Auu0GbEuSRCO\nSzRXQq/i9gZ5/fN96DQqrj/bLhorhUvUJTA4pfCkN6X2HSin5tG/I4fDZN91F/rsHAAyFNZUfdOY\nIhs/uWY0j72znVcWldLQ4uWqswahUvX+Y1ySJDJunE/l735Dw8svok5IIGH02FiXJQjHJGbwCr3K\nW1+U0eEJcvG0/thO8e4qQdn8VZVU/+NhIn4/WbfeTsLIUbEuqccMzLbyyxtKyE4zs3hjFY+/twN/\nIBzrss4InS2dnPt+jKTVUvfUk3Tu3B7rkgThmERzJfQaeytaWbW9jrz0BOaW5MW6HCEG/LU1VP/9\nr0S8HjJvvoXEkvGxLqnHpSUZ+fl1Yyjul8zWsib+8fY2fIFQrMs6I4wDB5Fzz30gSdT+6zE8e7s/\nH08OhehYv47qR/5O9cN/pfGN13CtXI63vJxIIBDFqoW+SNwt2Msp6c6L7mpyeVm9o54d5c1EIjJq\nlYRKJR1eBPSXN5bQP8sS1Rp6Y66xdrqZ+qurqP7Hw4RdbaRffxNJM2b2XHFxKBSO8MzHu9mwt5Gi\nvCTuu2LEUTcP743HaufO7dQ89iiSRkPm/FvQZWSiMptRJySg0umQIxHkYBA5FCLkctG+ZhXtq1cR\n7mg/6utpkpPJ/8Vv0CQldbuG3phrPFBSruJuQUHxQuEIm0udrNxWy+6DrchweO2qSEQmHJGRZTh/\nYr+oN1ZC/Glfv5aGF55DDgSwXX1tr2+sADRqFbddNAQZ2Li3kUfe3n7MBqu3MQ8bQfYdd1H75L+o\n+/e/jvyiJMFRBg1UZjNJc88hafoM1EnJBGpr8NdU4927l471a2l87WWy77rnDL0Dobfr/T+FguLJ\nsswT7+9ka1kTAINyrUwbkcW4welH/CKJyLJYYqGPkUMhnO++TdvihagMBjLvuofEMX1norNapeL2\ni4YAfa/BShg9lrz/93907t5FpNNNuLOTcGcnEZ8PSaM5/KHS6TEPH0FCSQkq7ddLbxgHDsI4cBDW\nqdMJtbbg3ryJjk0bSRxbEsN3JfQWvf8nUFC89Xsa2VrWRFGulRvPG0xW6tE3BhaNVe8hRyJ0btuK\nLisbXWbmUR8TcDbS8Px/8JY60GVmkX33Peiyss9wpbGnVqm47cKvG6zfv7SJS2cMYNSgtF5/t6yx\nsAhjYdFpvYakUpFx43wqHvgVja+9jGlwMWrzqW0+LghfEc2VENc6fUFeX1KKVqPi5u8Uk55sinVJ\nQpRF/H7qn3sG96aNAOgL+mOZOJnEceORw2HcGzfQsXEdvvJyABLGlpA5/3uoDH337lCNuqvBSjBo\nWL61lsfe3cHAbAuXzhiIzZYY6/Lini4zi5QLL6b5/Xdxvv0GmTd9L9YlCQonmishrr39xX7aPUEu\nmzFANFZ9QMjlovbxR/EdKMdYWISkN+DZvRPnwQM433zt67k0koSpeCiWyZNJnDi514/QdIdGreKG\ncwcze2wuH6w8wKZSJ399fQsL1lcyqTiDMUU29Dp1rMuMWynnnId743raV63EMmESpuIhsS5JUDDR\nXAlxq7SqjRXbasm1mTlnfH6syxGizFNZReUfHyLU1IRl0hQybpyPpNEQcrno2Liejg3rkTQaEseO\nI2FsCRqLuHHhaHJsCdx96XAO1LXz/opydu5vZuf+ZvRaNWPtNiYNzWRQjvWojZbHF2J/rYtQOEJR\nXhJmgzYG7yA2JI2GjBu/R+Xvf0vDS8/T74GHUOn1sS5LUCixFEMvp6TbWr8pGIrwwPPrqW/28PPr\nxzIw5+Q2q402peYar7z7y6j9598Jd3pIvfgSUi64SIxG9ZAgEp+u3M+anfU0uXxA1w11mSkm+mUm\nkpeeQGu7n9LqNqoa3d8cHKRfRiLFBcmMGJBKUV5Sn/g3cb79Jq0LF2AeNZrsO7+PpD76aJ84B0SH\nknIVSzEIirNgXQV1zR5mjcmJu8ZK6FkRv5/6Z54i7PWR+b3bsEyaHOuSepVsWwLzpg3g4qn9Katx\nscnh5GB9B5UNHdQ1e1i7qwHouqxYmGOlMC8JjVrFnoMt7K9t52B9BwvWVjJ9ZBbXnW1Ho+7da0+n\nzrsUf2UFnVu30PDKi2TcML9PNJVCzxLNlRB3Glo9fLKmAmuCjsumD4x1OUKUNb37NsEmJzmXzsMs\nGquokSSJwtwkCnO7FsqMyDLOVi9VjW4sZh39sxLRar4epbl4an/8gTCl1W28t7ycFdvqqGv2cPcl\nw7GYdcf6Noqn0mrJuuseqv/6J9pXrkBjtZI277JYlyUoTO/+E0RQHFmWeXVxKaFwhO/OLsRkEP1/\nb+Zx7KVt6RJ0mVnkf/eqWJfTp6gkiYwUEyWD0ynKSzqisfqKXqdm+IBU/u+6MYwvTmdftYvfvbiB\nivpjX7aRZZmWdh/Nhy5BKpHaaCTnBz9Ca0un5ZOPaVu6JNYlCQojfnMJcWVzqZOd5S0MKUhm3OD0\nWJcjRFHE76fhhedAksiY/z1UOh3gj3VZwlHotWpuv2goubYE3l9Rzh9f2cTQ/ilYzDosJh0Wsw63\nN8iBuq7LiO2dXXv1XTFzIOdOyFfkZTWN1UrOD++n6o8P0fj6q6itSWKBUaHbRHMlxA1/IMzrn+9D\nrZK4dm6RIk/IQvc1vf8uQWcjyeeci3HgoFiXI5yAJElcMLmAXFsCz/13D1v2NR31cSkWPWOLbJTX\ntfP2sv00tHoUO1dLl55Ozg9/TNWf/0D9c8+iz8k95qK2gvBNorkSzrhIRMYfDGPUH3n4fbzmIC3t\nfr4zqd8xV2EXegfvvlLaPl+MNjOT1IsvjXU5wkkYVZjGI/dOxeML4eoM0H7ow6BTU5BlwXpoPlZr\nh59H39nGim11ONt83HXJMEUu7WDI70fGDfOpf+bf1D75OPm/+PWhUVZBODbRXAlnjMcXYtX2WpZs\nqqbZ5WPkoDTOGZ9HUV4S9S0eFq6vJNWi54LJBbEuVYiiUHs7dc8+BUDm/FvELyoFUkkSCUYtCUYt\nOWlH/0MoOVHPz64dy1Mf7WJrWRN/eHkTP75qFCkWwxmu9vRZJkzEu68U17KlNL72Cpk33RzrkoQ4\nJ5orIepqmjpZtrmGVTvr8AfC6DQqcmxmtpY1sbWsiX4ZXdtzhCMy351ThF4rVpHurSLBALX/+ieh\n5mZSL75EXA7s5fQ6Nd+/dDhvfVHGog1VPPL2dn5+/RhFbixtu+pqfOX7aV+1AmNhEbZ558W6JCGO\nKe8IF+KeLMvUODvZ6Ghko8NJbVMn0PWX7IWTC5g+MpsEo5ayGheL1leyqdSJLMOIgamMLkyLcfVC\ntMiyTMMLz+HbX0bihImkXHBRrEsSzgCVSuLq2YUEwxG+2FzD0x/t5vuXDkelUtacSpVWR9add1P5\nu9/Q+OpLZI0eCqbkWJclxCnRXAmnTZZlGlq97KtqY1+Ni9KqNhpbvUDXwoSjC9OYODST0YVpR0xq\nHZRjZdAlw3G2edlS6mTi0Ewxib0Xa/n0YzrWrcUwcBAZN90s/q37mO/OLqShxcPWsibeXb6fK2Yp\nb9RSZ0snY/4t1D3xGHv/8Ccy7/0xunRxV7PwbaK5Ek6Zs83L55uqWburnnZP8PDnDTo1JXYbJYPT\nGT4g9VsT1/+XLcnI2WLvwF6tY8N6mj94D01aGtl334tKK+ZZ9TUatYo75w3joZc2sWBdJVmpZqaO\nyIp1WSctccxYAhfNo/mjD6j644Nkf/8H4vK28C2iuRJOiizLlFa1sXhjNVv2dV3Os5i0TBiSwaAc\nK4W5VnJtCYob8heix1teTv1zz6AyGMi55z6x4XIfZjZoue/yETz00kZe/Gwv6clGivKSYl3WSUu9\naB7W7HTKn3qG6r/9mcxb7yBxzNhYlyXEEdFcCYdFIjIVDR2oVRImgwazQYtep6ax1UtpVRuOyjZK\nq9pobu9aebkgM5G54/IYNzhdkWvYCNEXbG6m9vFHkEMhsu78Pvqc3FiXJMRYRoqJuy4Zzt/f3MoT\nH+zkt/PHYU3Qx7qsk5Z13jn4dGbqnnqCuicfJ3TFVSTNORtJJc6FgmiuBMDtDbJyey1fbK6h6X+2\nrJAA+Rv/32zQML44ndljcxmUYxXzZoRjivi81Dz2COH2dmxXX0vCiJGxLkmIE8X9krli5kDeWFrG\nUx/t4v6rRytytDthxEjyfvIzav75CM633sC1ZjWpF1xIwpiSHm2yZFkm1NqC78AB5GAAfV4+usws\nJLW4szpeieaqj4rIMmXVLlbtqGPd7gaCoQg6rYopwzMx6jR0+kJ4fEE8/hDJiXrseUkU5SWRlWZG\nJRoq4QTkSIS6Z54iUF2FdeZZJM2eE+uShDgzd1wejqo2tuxr4sNVB7hk+oBYl3RKDP0KyP/Fr2h6\n7x061q2l7t9PoMvKJuU7F5A4fuIpN1nB5iY61q/Hu38fvgPlhF2uI74uabXocnIx9B9A0qzZ6LOz\ne+LtCD1EkmX5xI86M2Sn89ibgQqnxmZL5KtcI7LM/hoXG/Y2ssnhpLWjax+39CQjZ43JYcqILEWu\noBwL38xV+Dbn22/QuvAzTMVDyfnBD5E0J/47TmQaHfGcq8cX5IHnN9Ds8vHDK0cybEBqrEvqtqPl\nGmiop+W/n9K+dg2EwySOn0DmLbd3u8EKd3bSsXEDHWvX4N1XevjzmuQUDAX9MfTvj2Qw4K+sxF9Z\ngb+mGsJhkCQSx08g9YKL0GUpu8mK5+P1f9lsicccaRDNVS/31YHq9gZ5+M2th3ezNxs0jC60Mb44\nnSH9U8Ro1ElS0gngTHOtWE7DS8+jy8wi7+e/RG3q3lZGItPoiPdcD9a384eXN2HQaXhg/jjFrOB+\nvFyDTU7qnn0aX9k+rNNnkn79jcedQhEJBmj59BNaP/svcigEkoTRPhjLhImYh49Ek3T0Sf9yKETn\njm00f/Qh/qrKQ03WRJLPORdDfr8eeZ9nWrwfr98kmqs+zGZLpKKqlb++sYWK+g5GF6YxY1QOQwqS\nxST006CkE8CZ5Nm7h+p//A2V0Uj+z399UmsAiUyjQwm5frG5mpcXlTIox8pPrhmtiHPTiXINezqp\n/uuf8VdVknzu+dguv/Koj/OUOmh46XmC9fVoklNIOmsOiRMmoE3p/iieHIng3rqF5o8+IFBdBYC+\noD9J02eSOH4CKoMyGlZQxvH6FdFc9TKhcISPVh9gx/4WAqEwwVCEQCiCVi0xd1w+Z43JOXxySrAY\n+fm/VlFW42LqiCxuOm+wGKXqAUo6AZwpgfp6Kv/wIBG/j9wf/wRTkf2kni8yjQ4l5CrLMk9/vJt1\nuxuYMzaXa+YWxbqkE+pOrqH2dqr+/AeCDfWkXXo5KedfAHSNOAWdjbQuWYRr+TKQJJJmzSbt0stQ\nGYynXJMcidC5fRuuFcvo3LEdZBlJb8AyYSLW6TPQ9yuI+5uQlHC8fkU0V72Iy+3nyQ92UlrtQqNW\nYdCp0WlVaDVqXG4/vkCYjGQjV84axND+KTzx4S62lzUxYUgGt14wRJF35MQjJZ0AzoSw203lHx8k\n2NBAxvzvYZ0y7aRfQ2QaHUrJ1RcI8fuXNlHT1MmtFw5h0tDMWJd0XN3NNdjcTNWff0+opQXj4GJC\nrS0EnU6IRADQZeeQceP8Hl+INNjSQvvqlbhWriDU0gyAPr8f1mkzSJwwEbXJ1KPfr6co5XgF0Vwp\njtcfYmtZEzarkYKsxMOjUGU1Lp54fwdt7gAldhvzzy8+YvXzDk+Aj1Yd5IstNURkGYtJS7snyOjC\nNO6cN0wRQ+1KoaQTQLTJoRDVjzyMd++e417+OBGRaXQoKdeGFg+/e3ED4bDMz68fS/6hTd3j0cnk\nGqivp+qvfyLsakOVkIAuIxNdZhaGgv5Yp03v1g0fp0qORPDs2olrxXLc27ZAJIKkN5A06yySzzkX\nTWJ8LeqrpONVNFcKUu1086/3dtBwaG8+nUbFgGwLmalmVm6rJSLLXDFzEOeMzzvm8G5dcydvLS1j\n2/5mxtjTuf3CIWg1orHqSUo6AUSTr+IgzrffxLt3D+bRY8i+8/unfOu5yDQ6lJbrln1OHnt3B7Yk\nA7++aVzc3sF8srnKoRARnw91QkIUqzq+UFsb7WtW0bp0CeG2NiSdjqSZh5osa3yslK+k4/W0miu7\n3a4CngBGAn7gFofDUfY/jzEBi4HvORyOvd15zlH0+eZq7a56XvhsL4FghFmjc1CpJByVbdQ43chA\nglHLnRcPpbggpVuv19DqoXigjZaWzugW3gcp6QQQDYG6Wpo+fB/3xg0AmIYOI/uue1DpT32l7b6e\nabQoMdf3VuznkzUVjBiYyr2Xj4jLeaJKzPUrkWCA9pUraFnwX0KtLUhaLabBxZiGDMU0ZCi67JyY\nzc1SUq7Ha666MxY5DzA4HI5Jdrt9IvAwcPFXX7Tb7SXAv4Hc7j6nL/MFQny+qRqXO0Cq1UCqxUCq\n1cCaHfV8vrkag07N3ZcMY6z967us3N4gFQ0d5KUnYDF1f8PbjGQTanEpUOhBsizjfPN12j5fDLKM\nvqA/aZdejql4SNxPlBWUY97UARys62D7/mbeWbafK2eJjZF7kkqrI+msOVimzaB9zSraliymc8f2\nrknwgNpqJfnsc0k++1zxc32KutNcTQU+A3A4HGsPNVPfpAcuAV4+ief0ObIss3ZXA28tK8PlDhz1\nMTlpZu6+dDiZKUdONEwwahnazdEqQYgm18rltC1ZhDYjE9vlV2AeNUacfIUep1JJ3HbRUP7w8iY+\nW1eJzWpg1hixL2VPU2m1JM2YRdKMWQRbWvDs2YVn9246d26n6e03iXg8pM67VPyMn4LuNFcW4Jvr\n7oftdrvG4XCEABwOx2oAu93e7ecci80Wv5MXT8e+qlaefn8Heyta0WlUXD3XzvihGThbvTS2enG2\neTDqNVw+qxCDvucnNvbWXGOtr+Xqraun7K03UJvNjPzD79Cn9fxq2n0t0zNFibnagAfvmMz9/1zB\nq4tLGZCfQklxRqzLOoIScz0mWyLY+8G88/E7m9j5q9/Q8unHGPVq+t1w3RltsHpDrt35Td4OfPOd\nqk7UJJ3icxRznbW76po7+XDVAdbvaQSgZHA6V84aSJq1ax2TpKxECrO+jqmj3UtPJ6Ck69dK0tdy\nlSMRqv72CBGfj8xbb6dd1kEPv/++lumZouRc1cD3Lx3OX17bwp9e3MD/XTuGfpnx8YtXybmemJ6s\nH/2U6r/9mZr3PsDT4SXtyqvPSIOlpFyP1wR2Z0LOauB8gEPzp3ZE6Tm9RpPLy3Of7uGXz65j/Z5G\n+mUm8pPvjuauecMON1aCoCStCxfgK9tHQsk4EsdPjHU5Qh8yMNvKbRcOIRAM88g722h2+WJdUp+g\nTU4m7//9H7qsbFoXL8T55uuxLklRujNy9T4w1263rwEkYL7dbr8GSHA4HE939zk9Um2cc7n9fLKm\ngmVbawhHZLLTzFwybQBjitLENWtBsfxVlTR98B5qaxIZ1x1/jzRBiIax9nSuOmsQbywt46mPdvF/\n144RCyKfAZqkJHLv/ynVD/+FtiWLMPQfgGWC+OOqO07YXDkcjghwx/98eu9RHjfzBM/ptdzeIAvW\nVfD5xmoCoQi2JAPzpg5gwpAMcQIQFC0SDFL37NMQDpN5080xXaNH6NvmjsujvK6d9Xsa+Wx9JedP\nVObGxEqjsVrJvvteKn73axpfeRHjoEK0qT0/37K3id6ysL2YxxeipslNjbOTykY363bX4/WHSU7U\nc/XkAqaOyBKroQu9QtM7bxGoqcY6Yxbm4SNiXY7Qh0mSxHVn23FUtvHBynKGD0glL100+2eCLiOD\n9KuuoeGl56l//llyf/T/Tnmx4L5CNFfdFI5E+HJnAwvWVVDX7DniawlGLVed1Z9Zo3PQadUxeQfd\nOQAAIABJREFUqlAQepZ7yybaPl+MLjsb25VXx7ocQSDBqGX++YN55O3tPPvJbn51Y4n4Q/YMsUyb\njnv7Vjq3bqF18UJSzjkv1iXFNdFcnUBEltmwp5EPVh2gocWDRi0xtCCZHFsCubYEcmxmcm1mtBrR\nVAm9R7C5ifrn/4Ok05F1+92ntfK6IPSkEQPTmD4ymxXbavlw1QEumzEw1iX1CZIkkXHjfCrK99P8\n/ruYhwxDn5cX67LilmiujqOmqZOnPtxJtbMTtUpi5qhsLphcQIrFEOvSBCFq5FCIuqeeJOLxkHHT\nzehzcmJdkiAc4aqzBrH7YAv/XVvByEFpDMqxxrqkPkGTaCHjpu9R+89/UPfsU+T/8teotN3fNaQv\nEeOpx9Da4ecfb22l2tnJ5GGZ/P62idxw7uC4aawigQBhj+fEDxSEk9T0wXv4yveTOGEilinTYl2O\nIHyLUa/hlguGgAxPvL+DA3XtsS6pz0gYMRLrrLMI1FRT/+zTyJFIrEuKS6K5OgqvP8Sjb2+jpd3P\n5TMHcssFQ0hPip/1qWRZpvrhv3DwVz8j3KGMxdYEZejYtJHWz/6LNj2DjOvFsgtC/CrKS+Lq2YW4\n3AH++MpmVu+oi3VJfYbtyqsxFtlxb9qI841XkWU51iXFnT7bXEUiMo7KVqoa3Ud8PhyJ8OSHO6ls\ndDNjVDbnTciPUYXH1rl9G779ZYRdLpxvvxHrcoReou2LpdT9+19d86zuuAuVIX7+oBCEo5k7Lo8f\nXDESnUbFfz7dw2tLSgmFxUhKtKm0OrK/fy+6nFzaln5O64JPY11S3Olzc66cbV5Wbq9j9Y46Wjv8\nAOTaEpgyPJOJQzP5YGU5O8tbGD4glevOLoq7v9xlWab5ow9AktDa0mlfsxrLpCmYiofEujRBoeRI\nhKZ336Z14QLUiRay77kPQ75YQ0hQhhEDU/nVTSU8/u4OlmysprrRzR3zhmExiblA0aQ2mcm578dU\n/fFBmt57B7U1CeuUqbEuK270mZGr/bUu/vbGFn767y/5ZM1BvP4Q00dmMbowjbrmTt5cWsaPHlvF\n8q215GckcMfFQ1HH4Toendu34a84SMLYcWTdfidIEg0vvUAkEIh1aYICRYIB6p7+N60LF6DNzCTv\n57/EOGBArMsShJOSkWzi59ePZUyRjb2VbTz4wgYq6sWUiWjTJieTc9/9qExmGl58Dvf2bbEuKW6o\nH3jggVjX8JUHPJ6ebxBcnQFeW1zKq4tLcbb5KMq1Mm/aAG4+v5iSwelMGJLBrNE5pFoMdHgDmI1a\nfnjFKBJNOmRZJtjYSNjtJuzxEPF5kQN+JJ0+JiNasixT/8xThNtdZN1+J4a8fCI+H507toEsH3X0\nymzWE41c+7rekGugoYHaxx7Fs3snxsIicn/0/9AmJ8esnt6QaTzqK7lqNSpKBqejVkls2dfE6p31\npFkNUVtotK/keiKaxESMg4roWPcl7o3rMQ4chNZmO+XXU1KuZrP+t8f6Wq+4LFhW4+L9FeW0dvgP\nrTuVQK7NTLPLx4erD+D1h8m1JXDd2UUU5SV96/mJJh2zx+Yye2wuAMGWFpo/XUT7qpUEnY3ferwm\nJRXLxEkkTpyMPjv7pGqVIxECdXVoLBbUiSe3u3vntq1do1Yl49HndNWaevEldGzaQMvCBSSOmyDW\nHRFOSJZlXCuW43zzNeRAAMukKaTfcKO4pVpQPJUkceGU/uRlJPLMx7t45uPdVNR3cMWsgXF5JaK3\nMBYWkn33vdQ+/ig1jz1C7g/vx1hYFOuyYkqKo1n+stP57WHc1g4/7y7fz7ayJorykpgwJIORg9LQ\na9U427y8s2w/G/Z2NUBGvRqvP3zE8016DZdMH8DM0dkn/OHq3LmD1iWL8ezaAbKMpNNhHjEStcnU\ndbtpOEIk4MezaycRX9fO7PqC/pjsdiS1BtRqJJUKSa1GZTKhMplQm8xIWi3+ioN4Sh14Sx1EPB4k\nvZ7Ui+aRPHsukubEPa4sy1Q++AD+qkr6PfDQEWsPde7YTs2jf0ffr4Dks89FpdMh6XSojEZyxw6j\nudV7wtcXTo7NlsjRjtd4F3K5aHjxOTq3b0NlMpFx3Y0kjp8Q67IA5WYa7/pqrnXNnTz+3g7qmj2M\nL07ntguH9uher3011+Nxb91C7ZOPo9JqyfnRT05pioGScrXZEo95QMVtc+UPhvlsXSUL1lUQCEYw\nGzR0+kIA6LVqCvOs7K1oJRSW6Z9l4aqzBlGYa6W1w0+1s5Map5tgKMLMMTknnNjor6rC+c6beHbt\nBMAwYCCWqdNILBmP2mT61uMjfj/ubVvo+HINnbt2wkms86FNs2EYOAjPrp2E3R3osrNJv+Z6TIOL\nAQh7vQTqagm1tKBNS0OXlY1Kr+86aB9/lMRx48m6/a5vvW7d0/+mY/3ab30+obAQ2613ok1J6XaN\nwokp6QTwFU+pg7onHyfc0YGpeAgZ82+Jq+NCiZkqQV/O1esP8cjb29hX7WLK8Ezmn1+MqoemdPTl\nXI+nY+N66p56suuP+/t/etI3xygpV0U0V4vWVcjOJje+QBiPP8SGvY20dvixmHVcOn0AU4dnUdfc\nybo9jazf3UBjm5dUi4HLZg5gfHHGKf3ABFtbaf7wPdpXr+qaszRkKGmXX3lSB0Oovb3r0mEk0jW6\nFYkgh0Jdc7Q8nYQ7O4n4fOhzcjAWDT68m3jY7abp/XdxrVgGsoy+oD+htlbCbW1HfoNDdwVG/D7C\n7e30++1D6LO/vWK2HArh3rKZsKcT2e8nEgjgqzhI55bNqBMtZN1xFyb74JPOSDg6JZ0AANpWLKPx\n1ZcBsF12JUlz5sbdxqtKy1Qp+nquXn+Iv76+hYP1Hcwak8N1c3vmLvC+nuvxtH+5hvrnnkGdmEi/\nX/8OTdK3p+Mci5JyVURzdeGPPzyiEI1axTnj8zh/Yj+M+iMvm8myjNPlIzlBj1Zzcr8gQm2tuDdv\nomPzJryOvSDL6LJzsF15FeZhI07/jZwk34FyGl59Gf/BA2hSUtBlZaPLykKbkkqwyYm/uhp/TTWR\nzk4sk6eQefOt3X5tWZYJrV/FgedeAFnGdsVVJM05+4gTiyzLcbfchBIo5QQgh0I433qdtqWfo0pI\nIPuOuw+PksYbpWSqNCJXcHuD/OW1zVQ7Ozl3Qj5XzBx42uc9kevxtS5aiPOt17tulvnxT7o1/QWU\nlasimqulGyvlgC+EUa/GoNOQlmQ4pXVKvhrBca1Yhnd/GSqdHpXRcHhBRH91FRx6z4YBA7BOm4Fl\n8lQkdWw3Xo4Eg6i02qN+TZZlIm43KrP5pEcbbLZEKlZvpO7f/yLc3o4mJQU5HEEOdI1uqfQGrFOn\nkTRr9mnd4dHXKOEEEO7spO7f/8KzZze6nNyuRf9s6bEu65iUkKkSiVy7uDoD/PnVzdS3eJhTksvl\nMwai0576eV/kenyyLFP31BO4N24g+ZxzsV1xdbeep6RcFdFccYwJ7d0VqK/HtWoF7atXHt4SRpeT\nC3KEiNdHxOclEghgHDiIhLElJIweG1fzTaLlqwM11NZK/QvP4a+qQqXXd0141+kIOp2EO9pBkjCP\nGk3SjJkgqQi1NBNsaSHU2oouIxPLpMknNbTb28X7CSDkaqP6738jUFONedRosm65Le5XXI/3TJVK\n5Pq1lnYff3l9C42tXtKTjdx47mCK+53a8iMi1xMLe71U/v63BOvrybrz+ySOLTnhc5SUa69troJN\nTjo2bKBjwzr8lRUAqMxmrJOnYp0xE11mVjTqVJQTHaiRYBD3xvW0LlmMv+LgsV9IpcI8bDiWqdNJ\nGDGy20O8vVU8nwCCTU6qH/4rQWcj1lmzSf/utXE3v+po4jlTJRO5HskXCPH+igMs2VSFLMPUEVlc\nOWsQCcajXzk4FpFr9/hraqj8/W+RVCryf/kAuszM4z5eSbn2quZKDoXo2LSBtqWf49tf1vVJtRrz\n0GEkTphIwpixYr2eb+jugSrLMr79Zbg3bURlMqFJSUGbkoraasXrcOBavRL/wQMASFot2vQMdJmZ\n6DIy0aSkEPH7CbvdRDo7CXs8qM0mtKlpaFLTDt/1eLQ7L5UqXk8A/toaqv/+V8JtbaRccCGpF1+q\nmDl18Zqp0olcj+5AXTsvLNhLVaMbi0nLNXOLGDc4vds/LyLX7mtfu4b6Z59Gl5NL7v0/QZNoOeZj\nlZRrr2iuwh0dtC3/grZlS7vuqJMkTIOLSRw3gYQxY1EnRGcVXqXryQPVX12Fa/UqvI69BBoakP2+\n7j9ZrcZUPJTEkhISRo1R/L9XPJ4AvOXl1Pzz70TcbtKuuIqUc86LdUknJR4z7Q1ErscWCkdYtKGK\nD1cdIBiKMGJgKtefbSfVajjhc0WuJ6fxtZdpW/o5muQUsu64C+PAQUd9nJJyVVRz5a+uonXRQnwH\ny5FDYeRQCDkcIux2QziMymDAMnU6SWfNQZcev5Nz40W0DlRZlgm7XAQa6gm1tqAymlCbzajNZlRG\nE+FON8GmJkLNTQSbmvDs3XP40i0qFYaC/qj0epAkUKmQtFqSps/EPPzM37F5KuLpBCBHIrQuXkjT\ne+9AJELGDTdhnTYj1mWdtHjKtDcRuZ5YQ6uHlz5zsKeiFb1WzaXTBzBrTA4a9bEvp4tcT44cidCy\n4FOaP3gPVCpsV1xN0uw53xopVFKuimiuXDt2yuVvvItn53YAVEYjkl6PpNEgqTWojEYsEydjmTIV\ntTG+J+bGk3g6UAPORtybNuLetBHfgfKjPiZx0mTSr7om7ke24iXXUFsb9c89g2f3LtQWC5k334p5\n2PBYl3VK4iXT3kbk2j2yLLN6Rz1vLt1Hpy9EcqKeOWNzmTEqG5Ph2/OxRK6nxrNnN3VPP0m4o4OE\nkvFk3jT/iJttlJSrIpqr1RdfJgMYC4tIPvd8zMNHKGISbryL1wNVjkRAlg8vvBpoqKfhxefxVxxE\nnWgh/drrSBg7Lm7nC8U6V1mWcW/ZTONLLxB2d2AePoKM+begsRx7LkO8i3WmvZXI9eS0dwb45MuD\nrNxehz8QRq9TM21EFudN6Edyov7w40Supy7Y2krdU0/gK9uHPi+f7Ht/eHjTeCXlqojmau+f/iqb\nZs455nVY4dQo6UCVw2FaFy+k+cP3kYNBjIOLSZ49F/PIUXHXaMcq10ggQMfaL2ldsohAbQ2SRkPa\n5VcddXhdaZR0rCqJyPXUeHxBlm+tZcmmalo7/CQYtdxywRBGDOzaZUPkenrkUIjG11/BtXwZmuRk\ncu79Ifq8fEXlqojmitNc50o4OiUdqF8JNNTT+Norh/d61KSkkjRzFpap0+NmZOZM5xpqa6Nt2ee4\nli0j7O4AtZrEknGknH/hEZt4K5kSj1UlELmenlA4whdbanj7izJCYZnzJuRzyfQBZGVaRa6nSZZl\nWhcuoOmdt5D0BrLvuIuCs6YoJlfRXPVhSj6x+muqaVv6Oe1r1yD7/SBJGPoPwDxsOKahwzD0HxCz\nEa0zlauvsoK2xYtoX7+264YOs5mkGbOwzjyr1y2Cq+RjNZ6JXHtGRX0HT364k8ZWL4NyrPxs/nik\nUDjWZfUKHRvXU//s08iRCLmXX4p29ARF7Bgimqs+rDecWMMeD+1rVtOxcT2+8v0QiQBdNz1oMzLR\npqWhTU1Dm5qKJi0NbaoNbWoqKsOJb6c+VdHMVZZlPLt20vLfT/CWOgDQZmaSPOdsLJOmdN1l2Qv1\nhmM1Holce47XH+LFz/ayfk8jiSYdd148lMGnuMK7cCTv/jJqH3/08A4rhkGFWCZMInHc+Li9wUk0\nV31Ybzuxhj2dePbsxrNrJx6Hg1BzE3IodNTHqhMSUVutqE0mVEbjoY+u/1Yf/u+uBkwOhyEcQQ6H\nUZmMaNNsaNPSUFusR53LFK1cPaUOmt9/F+++UgBMQ4aSPPccTEOHxd28s57W247VeCFy7VmyLLNs\nay2vLe76Gb1mTiGzxuTGuKreIez1IpXupGbxUryOvSDLSDodqRfNI3nO2XG3M4horvqw3n5ilSMR\nwu0ugs3NR6yrFTz0v+F2FxGf7/Bm3SdL0mrR5eR2bfA9cdLhUaOeylUOhwk2NxOoq6Vt6ZLD88zM\nI0eRevElGPL7nfb3UIrefqzGisg1Ohra/fz++fW4vUFmjs7hmjmFx10XS+ier47XYGsrHeu+pPWz\nBYTdHehy88i4/sa4uulNNFd9mDixdjVgEb+fiNdDxOsl4vES/uq/fV5AQlKrkdQqUKmJHFoANdjc\nRNDpxF9T3TXfyWTCOmUa1hmzSO+XgbOuFTkYQA4Eifi8hDs7iXg8hD2dyOEwklaLSqtF0umQJBWh\njnbCLhehdhehNhdBZyPBJieEv563YSoeSuq8S+LqBHKmiGM1OkSu0WGzJbJnXyP/fHcH1U43RXlJ\nfO87xdiSxDqMp+N/j9ew243znTdpX7USJAnr9JnYrrw6LqZHiOaqDxMn1tMXamulbfkyXMu/INze\n3mOvq0pIQJeegTYjA11GJsYiO6Yie4+9vtKIYzU6RK7R8VWuvkCI/3y6h00OJ1qNiu9M6sd5E/LR\natSxLlGRjnW8ekodNL7yIoHaWvT9Csi55z40SUkxqPBrornqw8SJtedEgkHcm7tWmNfpNARlCUmr\nQ6XTojIYUZnNqI0mVGYTklqDHAp2jWoFAxAOo7ZYUFusaKxW1Bar2Gngf4hjNTpErtHxzVxlWWbt\n7gbeXFpGe2eA9GQj184tYviA1BhXqTzHO17lUIiGV1+ifeUKNCmp5PzgRzFdikY0V32YOLFGh8i1\n54lMo0PkGh1Hy9XjC/HhqgN8vqmaiCwzZXgm1821o9eJUazuOtHxKssyLf/9hOb330VlNJJ91z2Y\nioecwQq/drzmSsy+EwRBEIQeYDJo+O6cQn4zfxwFmYms3lHPgy9tpMbpjnVpvYYkSaR+50Iyb70d\nORik+pGHaXr/3a75q3FEjFz1cuKv1ugQufY8kWl0iFyj40S5BkMR3l5WxpKN1eg0Kq49u4ipw7MU\nv01VtJ3M8eopdVD7xGNE3G6QJEyDh2CZOo2EMWNQaXVRrlRcFuzTxIk1OkSuPU9kGh0i1+jobq6b\nHE6e++8evP4Q44vTuWZuERZT9H/xK9XJHq8Rn4+OjRtoX73y8PqAaouF1AsvxjptRlTXxhLNVR8m\nTqzRIXLteSLT6BC5RsfJ5Ops8/LUR7sor23HbNBw9exCJg/LFKNYR3E6x2ugvh7XyuW0LfsC2e9D\na0sn9ZJLSSwZH5VFmEVz1YeJE2t0iFx7nsg0OkSu0XHSIywRmSWbqnl/RTn+YJjifsnceK6d9GRT\nFKtUnp44XkPt7bR88hFty7+AcBh9fj8ybpiPoaCgZ4o8RDRXfZg4sUaHyLXniUyjQ+QaHaeaa5PL\nyyuLStm+vxmtRsV5E/I5f2I/dFpxRyH07PEacDbS/MF7dKxbCyoVKed/h9QLLu6xS4WiuerDxIk1\nOkSuPU9kGh0i1+g4nVxlWWb9nkbeWLoPlztAmtXAVWcVMqYorc9fKozG8erZs5v65/9DqKUZXW4e\nmTff0iNbi4nmqg8TJ9boELn2PJFpdIhco6MncvX6Q3y85iCLN1QRjsgMLUhmxqgchhSkYDLE1ybF\nZ0q0jtew10vT22/iWrEM1GqMhUUY8vLR5+ejz++HHA7jr6zAX1mBr6ICSa0m/bob0WdnH69W0Vz1\nVeLEGh0i154nMo0OkWt09GSudc2dvLZkH7sOtACgVkkU5loZPjCVcYPTSbP2nd0con28du7aifOt\nNwjU1sCx+h+VCiIRVEYjWXfcjXnosGPVKpqrvkqcWKND5NrzRKbRIXKNjp7OVZZlDtZ3sK2siR3l\nzRyo63ptSYIxhTbmlORSlJfU6y8bnqnjNeLz4q+qxldVgb+yEkmtQp/fD0N+P3Q5ubg3b6ThheeQ\nIxHSr7mOpJlnHX5u2NOJr7ycglmTRXPVV4kTa3SIXHueyDQ6RK7REe1cXZ0BtpU18cXmGioaur5P\nXnoC507IZ+KQjF7bZMXT8eot20ftv/5JuKMD64yZqAxGPI69+CsOgiwz5cN3j/mP0Dcv6gqCIAhC\nHLOadUwfmc20EVmU1bhYsrGaTQ4nz3y8m9U76rjp3MGkJfWdy4WxYBxUSP7Pf03NY//AtXxZ1yfV\naoyDCjEOLj7uc0VzJQiCIAhxSpIkCnOTKMxNoqnNy8uLStlR3syvnlvPFTMHMnN0DqpeOooVD7Q2\nG3n/90vcGzegSU3FOKgQlV5/wueJjZsFQRAEQQHSkozcd8UIvvedYtSSxCuLSvnLa1vExtBRpjaZ\nsE6fgXnosG41ViCaK0EQBEFQDEmSmDI8i4duncDowjRKq9r4zXMbeGWRA7c3GOvyhENEcyUIgiAI\nCpOUoOf7lw7n3stHYEsysHRzDT976kuWbKwiHInEurw+TzRXgiAIgqBAkiQxalAaD94ygavOGkRE\nhteW7OPhN7bi8YlRrFg64YR2u92uAp4ARgJ+4BaHw1H2ja9fCPwaCAHPORyOZw59fjPQfuhhBxwO\nx/werl0QBEEQ+jyNWsU54/OZNCyTFxfsZcu+Jv7wymbuu3yEuKMwRrpzt+A8wOBwOCbZ7faJwMPA\nxQB2u10L/AMYB3QCq+12+0eAC5AcDsfMqFQtCIIgCMIRLCYdd18ynLe+KGPRhioeenkTP7h8BP2z\nLLEurc/pzmXBqcBnAA6HYy1Q8o2vFQNlDoej1eFwBIBVwHS6RrlMdrt9kd1uX3qoKRMEQRAEIYpU\nKomrZxdy7dwiOjwB/vzqZraUOmNdVp/TnZErC10jUV8J2+12jcPhCB3lax2AFfAAfwOeBQqBBXa7\n3X7oOcdksyWeTO1CN4lco0Pk2vNEptEhco2OeM716nOLGZCXzF9e2cjj7+/glouGcdH0gbEuq1vi\nOdfu6k5z1Q58852qvtEk/e/XEoE2oJSuES0ZKLXb7c1AFlB1vG8UL0ve9ybxtJVAbyJy7Xki0+gQ\nuUaHEnLtn27mp9eM5tG3t/PMhzs5UN3G1bMLUanid9FRJeT6leM1gd25LLgaOB/g0OW9Hd/42h6g\n0G63p9jtdh1dlwS/BG6ma24Wdrs9m64RrrpTKV4QBEEQhFNTkGnhlzeUkJNmZsmmah5/bwf+QDjW\nZfV63Wmu3gd8drt9DV2T139ot9uvsdvttzkcjiDwI2AhXU3Vcw6Howb4D5Bkt9tXAW8CN5/okqAg\nCIIgCD0v1WrgZ9eNZUhBMlvLmvjTq5tpbPXEuqxeTZJlOdY1fEVWylCgkihpiFVJRK49T2QaHSLX\n6FBirqFwhJcXOli5vQ69Ts21c4qYMjwTKY72JlRSrjZb4jGDE4uICoIgCEIfoFGruOm8wdx64RBU\nEjz33z08+cFOsW1OFHRnQrsgCIIgCL2AJElMGppJYY6VZz/ZzUaHk7IaF5fNGMiEIRlo1GLMpSeI\nFAVBEAShj0lLMvKTa8Zw2YwBdHiC/OfTPfz86bV8sbmaYEhMeD9dYuRKEARBEPoglUriO5MKmDgk\nk8/WVbJiey0vLyrlo9UH+c6kfswcnSNGsk6RSE0QBEEQ+rBUq4Frzy7iL3dO5ryJ+fiDYV5bso8H\nnt/AroMtsS5PkURzJQiCIAgCVrOOK2YO4k93TGL6yGzqmjp5+I2tPPbudhrbvLEuT1HEZUFBEARB\nEA6zmHTcdN5gZo3O4dUlpWzZ18SO8mZmjs7hgkkFWMy6WJcY98TIlSAIgiAI39IvM5GfXTuG2y4a\nQlKCniUbq/npU1/ywcpyvH6xLvjxiJErQRAEQRCOSpIkJg7JpMSezvKttXy85iAfrT7I55uqGT4w\nleL8ZIr7JZOWZIx1qXFFNFeCIAiCIByXRq1i9thcpgzPZPHGapZuqmbtrgbW7moAIM1qICfNTLLF\nQEqinhSLnvz0RHJs5rhaAf5MEc2VIAiCIAjdYtBpuHByARdM6kdts4e9Fa3sqWjFUdnKtv3N33p8\nZoqJ8cXpjCvOICfNHIOKY0M0V4IgCIIgnBRJkshJM5OTZmb22FxkWcbrD9HS7qelw0dzu589B1vY\nvr+Zj1Z3XUrMsZkZNzid8cUZZKaYYv0Woko0V4IgCIIgnBZJkjAZtJgMWnLTEwCYNToHXyDE1rIm\nNuxpZEd5Mx+sPMAHKw+Qn57AuOJ0Jg3NJMViiHH1PU80V4IgCIIgRIVBp2HikEwmDsnE4wuxZZ+T\nDXsb2XWghcrl5by3vJwhBclMGZHFmEJbrMvtMaK5EgRBEAQh6kwGDVOGZzFleBZub5BNjkZW76hn\n18FWdh1sxaTXMGNsLiWFaRRkJip6IrxorgRBEARBOKMSjFpmjMphxqgc6po7WbWjjjU761mw5iAL\n1hwkJ83M1BFZTBqaqchFSyVZlmNdw1dkp7Mj1jX0OjZbIiLXnidy7Xki0+gQuUaHyLXnhSMRqlt8\nfLpyP1v2NRGOyOi0Kq46q5CZo7LjbiTLZks8ZkFi5EoQBEEQhJhTq1SUFGfQL82E2xvky531fLT6\nAC8vdLB1XxPzzx9MUoI+1mV2i9j+RhAEQRCEuJJg1DJ3XB6/+94EhvZPYUd5M7/+z3o27m2MdWnd\nIporQRAEQRDiUnKinh9dOZJr5xbhD4Z54oOd/OmVTWwrayKOpjV9i7gsKAiCIAhC3JIkidljcxlS\nkMybS8vYvr+Z0ne2k2v7/+3dfZBVdR3H8fcuLAvu8iiCGIrY4FfTIcbGh0xGm3yA1Cl7JLWnGdTK\nKS1nNEzNGsppSqfIKccaR7Map2wax1KqMVOxKRsh04G+oAiBWIAiD6ssuGx/nLOIsNcueVi4y/s1\ns8Pds7979/CZe/f32d89e04b00+awPFHjWHggH1rrchyJUmS9nnjDmzj8g+/nRWrN3H/X5fz2MLV\n/Ojehdwx958cOX4ER08YyVETRjJh7FCam/fuwe+WK0mS1DAOHdPOxecew3lTj+CBx1eAjgklAAAH\nvklEQVTy1LMvbv8AaG0ZwPgxbRw2diiHjWln/Jh2Rra3MqxtUJ+tcFmuJElSwzloxBBmvGcSAOs3\ndbLoX+tYtGwdS5/fwLOrNvLMcxt2uU/7kBaGtw1i7KgDOGR0G+MPauOQ0W0cPOqASouX5UqSJDW0\n4e2t2y+zA7D11S5WrulgxepNrFrbwfqOLazf1Mn6ji28uLGT59Z2MH/xmu33H9TSzFsPGc6k8cM5\n8tARHH7wUAa3DqT5/zy3luVKkiT1Ky0DBzBx3DAmjhu2y9e6u7vZ0LGFlWs7WLWmg5VrNrH0+Q0s\nWr6ORcvXvW7soJZmBg8aSGtLM01NTfRUrebmJm69+oya399yJUmS9htNTU0Mb29leHsrxxw+avv2\nTa9s5emV61m88iWeX9vB5i1dbN7aReeWLjq3drGte1sxsI4zQFiuJEnSfq99SAtTJo1myqTRb/qx\n9q0TQ0iSJDU4y5UkSVKFLFeSJEkVslxJkiRVyHIlSZJUIcuVJElShSxXkiRJFbJcSZIkVchyJUmS\nVCHLlSRJUoUsV5IkSRWyXEmSJFXIciVJklQhy5UkSVKFLFeSJEkVslxJkiRVyHIlSZJUIcuVJElS\nhZq6u7v39j5IkiT1G65cSZIkVchyJUmSVCHLlSRJUoUsV5IkSRWyXEmSJFXIciVJklShgX3xTSLi\nROBbmXlaREwBbgFeBRYDMzNzW0RcAZwPbAO+mZm/joghwE+BMcBG4JOZuaYv9rkR7JTrcRS5dgJ/\nBy4rc70IuIQi79mZ+Rtzra3OTL8IzCjvcl9mfs1M31g9uZbjmoHfAvdk5i3m+sbqfL5OB74KNAGP\nA5cCgzHXmurM1TmrThHRAtwGHA60ArOBhcDtQDfwFHBpf5qz9vjKVURcCfyY4sUMxYv865l5CkXI\nZ0fECOAy4J3AmcB3y7GfBZ7MzKnAT4Br9vT+Nopecr0VuLzMaj1wfkQcDHwBeBdwFnBDRLRirr2q\nM9MjgAuAk4GTgDMjYjJmWlM9ue4wfDYwcofPzbWGOp+vQ4FvA+dk5onAMmA05lpTnbk6Z+2eC4EX\nylymATcDNwHXlNuagPf1pzmrL94WfAb4wA6fLwBGRUQTMBTYCnQAy4G28mNbOfYUYG55+37g9D7Y\n30axc67jM/PP5e1HKbI7AXg0Mzszcz3wNDAZc62lnkxXANMysyszu4EWYDNm+kbqyZWI+BDFa3/u\nDmPNtbZ6cj0ZeBK4MSIeAf5T/sZvrrXVk6tz1u75JXBtebuJYlXqHcBD5baerPrNnLXHy1Vm/oqi\nQPVYAswBFgFjgT+V21dQLBPOL78OMIziNwUolgKH7+HdbRi95Lo0Ik4tb59L8YLfMT94LUNz7UU9\nmWbm1sxcGxFNEfEdYEFmLsZMa6on14g4lmIF67qd7m6uNdT5M2A08G7gKmA6cHlEHIm51lRnruCc\nVbfM3JSZG8uV1LspVp6ayl9Qofe5qdb2hsh1bxzQ/j1gamYeRbG8dyPFi34cMBE4DHh/RJwAbKBY\n3aL896W+392G8WlgVkQ8AKwG1vL6/OC1DM21Pr1lSkQMBn5Gkd3nyrFmWr/ecv0E8Bbgj8CngC9F\nxDTMdXf0lusLwN8y89+ZuQl4GJiCue6O3nJ1ztpNEXEo8CBwZ2b+nNdW+6D3uanW9obIdW+Uqxcp\nggJYRXF8xTrgFaAzMzdTBDeCYgn2veXY6cAjfburDeVs4ILMfA9wIPAH4DFgakQMjojhwNEUBw6a\na312ybR8O/se4InMvCQzu8qxZlq/XXLNzCsz88TMPI3iINebMnMu5ro7evsZMB84NiJGR8RAiuME\nF2Kuu6O3XJ2zdkNEjAV+D1yVmbeVmxdExGnl7Z6s+s2c1Sd/LbiTmcBdEfEqsAW4KDOXRcTpwF8i\nYhswj+IJPA+4IyLmlWPPr/WgYgnwQES8DDyYmfcBRMQciidiM/CVzNwcET/EXOuxS6YRcR5wKtBa\n/hUWwCzATOvX63O1BnOtX62fAbOA35VjfpGZT0XEUsy1XrVydc6q39UUCynXRkTPsVeXAXMiYhDF\nYUJ3Z2ZXf5mzmrq7u//3KEmSJNXFk4hKkiRVyHIlSZJUIcuVJElShSxXkiRJFbJcSZIkVchyJUmS\nVCHLlSRJUoX2xklEJelNiYg7gUcy89by8weBLwOzKc6i/TLw+cxcUF638PtAOzAGuDEz50TE9RRn\nLD8MuDkzf9D3/xNJ/ZErV5Ia0W3AhQARMYGiNN0EXJmZxwEXA3eVY2cCszPzeIqLGH9jh8cZnJlv\ns1hJqpJnaJfUcMprPC4BTgc+TnmpDIrr5vU4CJhMcd23aeXtycCMzGwqV66GZOZVfbjrkvYDvi0o\nqeFkZndE3AF8DPgIcA5wRWZO6RkTEeMpLhR/N8WFdu+lWM2ascNDvdJnOy1pv+HbgpIa1e3AZ4AV\nmbkcWBIRPW8VngE8XI47A7guM++huOg2ETGg73dX0v7CciWpIWXmCmAFRckCuACYGRH/AG4APpqZ\n3cD1wLyImA+cBSwDJvb1/kraf3jMlaSGUx5zNQ54CDg2Mzv38i5J0nauXElqRB8EngBmWawk7Wtc\nuZIkSaqQK1eSJEkVslxJkiRVyHIlSZJUIcuVJElShSxXkiRJFbJcSZIkVei/+d5ogxTuuggAAAAA\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11eac25f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dny_ts.plot(figsize=(10, 8))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 变成女孩名字的男孩名字（以及相反的情况）\n",
    "\n",
    "另一个有趣的趋势是，早年流行于男孩的名字近年来“变性了”，列入Lesley或Leslie。回到top1000数据集，找出其中以\"lesl\"开头的一组名字："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "632     Leslie\n",
       "2294    Lesley\n",
       "4262    Leslee\n",
       "4728     Lesli\n",
       "6103     Lesly\n",
       "dtype: object"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_names = pd.Series(top1000.name.unique())\n",
    "lesley_like = all_names[all_names.str.lower().str.contains('lesl')]\n",
    "lesley_like"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "然后利用这个结果过滤其他的名字，并按名字分组计算出生数以查看相对频率："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "name\n",
       "Leslee      1082\n",
       "Lesley     35022\n",
       "Lesli        929\n",
       "Leslie    370429\n",
       "Lesly      10067\n",
       "Name: births, dtype: int64"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filtered = top1000[top1000.name.isin(lesley_like)]\n",
    "filtered.groupby('name').births.sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "接下来，我们按性别和年度进行聚合，并按年度进行规范化处理："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "table = filtered.pivot_table('births', index='year',\n",
    "                             columns='sex', aggfunc='sum')\n",
    "\n",
    "table = table.div(table.sum(1), axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>sex</th>\n",
       "      <th>F</th>\n",
       "      <th>M</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1880</th>\n",
       "      <td>0.091954</td>\n",
       "      <td>0.908046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1881</th>\n",
       "      <td>0.106796</td>\n",
       "      <td>0.893204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1882</th>\n",
       "      <td>0.065693</td>\n",
       "      <td>0.934307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1883</th>\n",
       "      <td>0.053030</td>\n",
       "      <td>0.946970</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1884</th>\n",
       "      <td>0.107143</td>\n",
       "      <td>0.892857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>131 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "sex          F         M\n",
       "year                    \n",
       "1880  0.091954  0.908046\n",
       "1881  0.106796  0.893204\n",
       "1882  0.065693  0.934307\n",
       "1883  0.053030  0.946970\n",
       "1884  0.107143  0.892857\n",
       "...        ...       ...\n",
       "2006  1.000000       NaN\n",
       "2007  1.000000       NaN\n",
       "2008  1.000000       NaN\n",
       "2009  1.000000       NaN\n",
       "2010  1.000000       NaN\n",
       "\n",
       "[131 rows x 2 columns]"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "table"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "现在，我们可以轻松绘制一张分性别的年度曲线图了："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11f0640b8>"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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O8uVLKV26DGPHfkHRosVYuHAeoaGhj/y5RbKbGzeus3HjegYMeJ7OnduzatWKRMcbN27K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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11f074748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "table.plot(style={'M': 'k-', 'F': 'k--'}, figsize=(10, 8))"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [py35]",
   "language": "python",
   "name": "Python [py35]"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
